The Identification of Communities of Practice in Web 2.0 through ...

Journal of Scientific and Technological Studies, (), -(00)



The Identification of Communities of Practice in Web 2.0 through Social Networks 





Stephen J.H. Yang , Irene Y.L. Chen and Jeff Huang ,



National Central University, Taiwan , Ching Yun University, Taiwan    [email protected] , [email protected] , [email protected]

Abstract This paper presents a three-layer social networks-based peer-to-peer search for identifying communities of practice in the Web .0. One of the essential goals of applying Web .0 services in communities of practice is to enhance interactive communication and collaboration among participants in the communities. In Web .0, participants are co-workers as well as co-authors. They can read and write to the Web, in which participants become the consumers and producers of information (knowledge) and services. The performance of collaboration is fundamentally based on how participants can be effectively found. As a result, one of the critical challenges of Web .0 services are the identification of communities of practice, that is how to identify the right participants to facilitate communication and collaboration in business domains. This paper presents a three-layer social network, in which we identify two important relationship ties – knowledge relationship tie and social relationship tie. The two relationship ties are metric used to measure the strength between pairs of participants on a social network. The stronger the knowledge relationship tie, the more knowledgeable the participants; the stronger the social relationship tie, the more likely the participants are willing to share their knowledge. Based on such social networks, this paper reports our experience of providing Web .0 services for identifying communities of practice through peerto-peer search. Key words: Web .0, communities of practice, social networks, peer-to-peer



Journal of Scientific and Technological Studies, (), -(00)

1. Introduction Web .0 has become a major technology that supports content publishing and sharing over the Internet. Web .0 refers to an expected second generation of Web technology that allows people to create, publish, exchange, share, and cooperate on information (knowledge) in a new way of communication and collaboration. The Web .0 technology makes the Web not only for browsing, but also for creating and sharing. The success of Web .0 heavily relies on communication and collaboration among people over the Internet -- where are the people; what people possess; whether people are willing to communicate; how a group of people can be formed as communities of practice; and how people can work together trough new generation of social software such as Wikis, Blogs, RSS feeds, video podcast, Ajax-based browsers, peerto-peer, instant messenger, and other social networking software. Some successful examples of Web .0 applications are Wikipedia, YouTube, MySpace, and Flickr. The Web .0 is shifting economical value of the Web to new business models for the next generation of Web technologies and applications (O'Reilly, 00). One of the essential goals of applying Web .0 services in communities of practice is to enhance interactive communication and collaboration among participants in the communities. By communities of practice, we refer a group of individuals with the same interests of a particular subject. By participants, we refer the individuals who either possess related information of interests, or can help to discover and obtain the information, or are willing to exchange and share information with others. In Web .0, participants are co-workers as well as co-authors. They can read and write to the Web, in which participants become the consumers and producers of information (knowledge) and services. As a result, one of the critical challenges of Web .0 services is how to identify the right participants to form communities of practice. This paper presents a three-layer social networks-based peer-to-peer search for identifying communities of practice in the Web .0. Here peers represent individuals (participants) who are associated with knowledge and social relationships. By analyzing and calculating these relationships among peers, our method provides a systematic way to identify communities of practice by discovering participants based upon configurable and customizable requirements. Social networks are built upon an idea that there exists a determinable networking structure of how people know each other. In such networks, people are connected through common social relationships either directly or indirectly (Churchill et al., 00). Researchers have recognized that a broader sense of social network is a self-organized structure of people, information, and communities (Raghavan, 00; Kaut et al., ). A social network can be modeled by a net structure consisting of nodes and edges. Nodes represent individuals or organizations. The edges connecting nodes are called ties, which represent the relationships between the individuals and organizations. The strength of a tie indicates how strong the relationship is. Many kinds of ties may exist between nodes. In this paper, we will address knowledge relationship tie and social relationship tie. In addition, social networks can be represented as matrices; therefore, the properties of the social networks can be analyzed by graph theory. Furthermore, the maximum size of a social network tends to contain around 0 nodes (Hill, and Dunbar, 00). In our research, we consider the scope of a social network to a PP network with about 0 to 0 peers within a university. A PP network is a distributed networking structure that treats every participant as a peer and allows each peer to play as either a client or a server under different circumstances (Brase, et al., 00, Aberer

The Identification of Communities of Practice in Web .0 through Social Networks



et al., 00). PP and social networks share many concepts in common. For example, they are both distributed networking structures; a peer in a PP is like a node in a social network; a link in a PP is like a relationship tie in a social network. In contrast to most PP searches that emphasize on search queries and protocols, our social network-based PP search is designed to potentially help in reducing search time and decreasing message traffic by minimizing the number of messages circulating in the network. Our three-layer social network identifies two important relationship ties – knowledge relationship tie and social relationship tie. The two relationship ties are metric used to measure the degree of peers’ knowledge matching with a query, and the degree of peers’ social relationship with other peers. We have developed a computational model to calculate the two metric and conducted experiments to evaluate how to improve the identification of communities of practice in Web .0 with such a social network-based PP. Communication and knowledge sharing are two of the successful factors of learning (Andrea, 2006). Thus, we need knowledge workers who are proficient in problem-solving, collaboration,

2. Social Network -based P2P

communication, continually learning and knowledge-creation in such an collaboration-based learning environment (Herrmann et al., 2003; Kienle et al., 2004). Collaboration learning involves multipleand communication, conversation, solving andfactors delivery media that(Andrea, are Communication knowledge sharing are twoproblem of the successful of learning 00). designed to complement each other and promote learning and encourage knowledge sharing Thus, we need knowledge workers who are proficient in problem-solving, collaboration, communication, (Arnseth et al., 2001). In this study, we identify two important relationship ties learning – knowledge continually learning and knowledge-creation in such an collaboration-based environment (Herrmann et al.,tie00; Kienlerelationship et al., 00). Collaboration involves communication, relationship and social tie for supporting thelearning collaboration and multiple communication conversation, problem solving and delivery media that are designed to complement each other and promote between learners (knowledge workers) in a social network (Tomsic et al., 2006). learning and sharing (Arnseth et al., for 00). In this study, we identify two important The encourage supporting knowledge activities such as group discussion collaborative learning vary widely relationship ties – knowledge relationship tie and social relationship tie for supporting the collaboration and (Fu-Hsiang Wei, 2006; Richard Joiner, 2004), but little formal research exists on how to communication between learners (knowledge workers) in a social network (Tomsic et al., 00). construct the most effective activity designs and how to form effectively participants in group The supporting activities such as group discussion for collaborative learning vary widely (Fu-Hsiang discussion for knowledge sharing and knowledge creation (Stahl, 2000) Wei, 00; Richard Joiner, 00), but little formal research exists on how to construct the most effective The keyand ideahow of our three-layer socialparticipants network-based illustrated inforFigure 1. For sharing a activity designs to form effectively in P2P groupis discussion knowledge and givencreation query requesting for participants with certain knowledge, a social network containing knowledge (Stahl, 000) relevant participants with the requested knowledge willPP be dynamically The key idea of our three-layer social network-based is illustratedconstructed in Figure within . For the a given query requesting with certain knowledge, a social network containing relevant participants with scopefor of aparticipants P2P network. the requested knowledge will be dynamically constructed within the scope of a PP network.

Figure 1. A three-layer social network-based P2P

Figure . A three-layer social network-based PP

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Journal of Scientific and Technological Studies, (), -(00)

As shown in Figure , the first layer is the PP knowledge net (K-net), which is established to connect peers who own the requested knowledge into a pool of active peers. In this research, we confine the scope of the pool to a small-scale PP network with about 0 to 0 peers within a university. A peer in the pool can be either a knowledge repository or a knowledgeable individual. A weighted edge between two peers is a knowledge relationship tie, which is used to measure the degree of how a peer’s knowledge matches the other’s query. For example, if a peer’s query in such a PP K-net (e.g., peer Steve) is requesting for peers with“Software Engineering”knowledge, a PP K-net will be dynamically generated based on the query. As shown in Figure , peers Chris and Albert are with knowledge relationship tie that are (0.) and (0.), respectively, which means Chris’s knowledge matches better with Steve’s request than Albert’s. PP social net (S-net) is the second layer. A weighted edge between two peers is a social relationship tie, which is used to measure the degree of social familiarity between the two peers. Peers on K-net without the requested knowledge will be removed from S-net (e.g., Mary). Using the example shown in Figure , Steve is more familiar with Albert than Chris because the social relationship tie between Steve and Albert is (0.), which is greater than the social relationship tie between Steve and Chris (0.). Based upon the generated S-net, an instant messenger-equipped group discussion, shown on the third layer in Figure , is invoked to help Steve communicate with the peers found in Layer- (Chris and Albert). Peers appear on S-net with negative relationship with the requester will be removed from the group discussion (e.g., Bob). This example shows that the essential challenge of constructing this three-layer social network is how to calculate knowledge relationship tie and social relationship tie.

3. Calculation of Knowledge Relationship Tie In our research, we consider a peer’s knowledge domain, proficiency, and reputation of contribution as key indicators determining its capability to participate in collaborations. Therefore, as shown in K-net, we calculate a peer’s knowledge relationship tie based on the three indicators.

. Rationale and Equations We use Bloom taxonomy matrix (Anderson et al., 00) to classify a peer’s domain knowledge and its proficiency in such a domain. As shown in Figure (a) and (b), Bloom taxonomy is a matrix consisting of two dimensions: Knowledge dimension and Cognitive Process dimension. Knowledge dimension indicates the types of knowledge; Cognitive Process dimension indicates cognitive processing of knowledge. Each cell in the matrix is associated with a value ranging from 0 to , representing the level of proficiency. For example, let Figure (a) and Figure (b) indicate peer Albert and peer Christ’s knowledge proficiency regarding the knowledge domain of“Software Engineering,”respectively. In Figure (a), the cell (Factual knowledge, Remember) has a value (0.), which indicates Albert is good at memorizing factual knowledge about“Software Engineering.”In Figure (b), the cell (Conceptual knowledge, Apply) has a value (.0), which indicates Chris is excellent at applying conceptual knowledge of“Software Engineering.”

The Identification of Communities of Practice in Web .0 through Social Networks



Cognitive Process Dimensions Knowledge dimension

Remember Understand

Apply

Analyze

Evaluate

Create

Factual knowledge

0.

0.

0.

0.

0

0

Conceptual knowledge

0.

0.

0.

0.

0

0

Procedural knowledge

0.

0.

0.

0.

0

0

0

0

0

0

0

0

Metacognitive knowledge

Figure (a). Example of Albert’s Bloom taxonomy matrix Figure 2(b). Example of Chris’s Bloom taxonomy matrix Cognitive Processmatrix Dimensions Figure 2(b). Example of Chris’s Bloom taxonomy Figure Figure 2(b). 2(b). Example Example of of Chris’s Chris’s Bloom Bloom taxonomy taxonomy matrix matrix Knowledge dimension Remember Understand Apply Analyze Evaluate Create Consider peer i’s query is requesting for peer proficiency conforms to a 0. Factual knowledge 0. 0. j whose knowledge 0. 0. 0. Consider peer i’sFigure query2(b). is requesting for peer j whose knowledge proficiency conforms to a Example of Chris’s Bloom taxonomy matrix requested knowledge domain Peer can calculated Conceptual knowledge 0.i’s query 0.j jbe .0 by: proficiency 0. 0. totoaa 0. Consider Consider peer peer i’s i’squery queryisisk. requesting requesting for forpeer peer whose whose knowledge knowledge proficiency conforms conforms 2(b). Example of Chris’s taxonomy requested knowledgeFigure domain k. Peer i’s query can beBloom calculated by: matrix Procedural knowledge 0.i’s 0.be 0. by: 0. 0 0 Figure 2(b). Example of Chris’s taxonomy requested requested knowledge knowledge domain domain k.k.Peer Peer i’squery query can can beBloom calculated calculated by: matrix Consider peer i’s yFigure query is2(b). requesting knowledge conforms to a 0 Metacognitive knowledge 0e (i ) T forofpeer 0 j whose 0taxonomyproficiency 0 0 Example Chris’s Bloom matrix KQ K (proficienc ( j ) x K (conformanc k) k) ( k ) (i , j ) proficienc yquery isconformanc e T for peer j whose knowledge proficiency conforms to a Consider peer i’s requesting KQ( k ) (i, j )knowledge Kproficienc ( j ) x Kconformanc (i )TTquery can Chris’s requested domain k. i’s calculated by: k) k ) Peer (proficienc (conformanc yy e e Example Figure (b). of jbewhose Bloom taxonomy matrix conforms to a Consider peer i’s is requesting knowledge proficiency 4 (query 6j))xxK KQ KQ ( ( i , i , j j ) ) K K ( j K ( (i )i ) for peer k k k k k k ( ( ) ) ( ( ) ) ( ( ) ) requested serialized knowledge§domain k. Peer ·i’s query can be calculated by: = KQ( k Consider ( i , j ) KQ ( m , n ) ¨ ¸ 4 6 ¦ ) requesting for peer j whose knowledge proficiency conforms to a ) peer i’s¦query( k is requested serialized knowledge§domain k. Peer ·i’s query can be calculated by: 44 1 ¨ 6n6 1 KQ m KQserialized (i, j ) = (mrequesting , n)·e¸¹· T for peer j whose knowledge proficiency conforms to a requested ¦ ( kconformanc )is (serialized k ) Consider §©§i’s proficienc y¦ peer query KQ (i, j()(i,iknowledge j1KQ )KQ xK KQ ,j j)K)=(= ()(mk. m, ,nPeer n))¸¹¸(i )i’s query can be calculated by: ¨ m 1¨© n( domain requested k k ( ) ) ( ¦ ¦ ¦ ¦ ( k ( k ) ) ( k k ) tie serialized reputation e proficienc©y( conformanc Kknowledge K ji)) T be calculated by: n in(1,Peer 1j))xxK ¹(¹(can i’s domain query KQ K j k ( k )( k()i ,(ij,)j ) KQ (( kk )) mm1 1©k. ( ) ( kreputation ) tie serialized proficienc y conformanc e 4 6 Ktie(tiek )(serialized (i, j ) KQ (·(ji)) T KQ K ((serialized ( k) ) §(i(,jj))xxKK(reputation k ) (i , j ) kk )) KK i,)i,j j)) (KQ i¦ ,i6,yj j)KQ )xxKK((kkk()reputation KQ iKQ , j )(serialized )(¸j j)e) T 4 (¨( ¦ k(= k) ) proficienc k)(conformanc ( k( k) )(( k( )m, n( §© n 1( j ) x K ·¹ (i ) serialized KQ m ( k ) (i , (ji), j ) K )4 1¨ ) , n) ¸ =( k¦ KQ 6 KQ( k ) (( km ¦ (k ) § · serialized where m 1© n 1 KQ KQ (i, j ) = (m, n) ¹¸ ¨¦ tie( k ) serialized ¦ ( k )reputation 4 6 K ( i , j ) KQ ( i , j ) x K ( where ( k ) serialized ( k ) m 1 © n§ 1 (k ) ¹ j·) tie reputation taxonomy representing a query by peer i that is requesting for (iis , jserialized )a =Bloom KQ (m, matrix n( )j ¸) ()i, j )KQ ¨, ¦ where where ¦ ( k ) ( k ) ( k ) KKQ ( i , j ( i j ) x K (k ) (k ) (k ) tie reputation matrix m 1 © n taxonomy 1 ¹) is serialized a Bloom representing a query by peer i that is requesting for ()i, j )KQ ( k ) KKQ ( i , j ( i , j ) x K ( j (k ) (k ) is a( ka)Bloom Bloom taxonomy taxonomy matrix matrix representing representing aa query query by by peer peeri iknowledge that that isis requesting requesting for KQ KQ ((i,i,j j)) ispeer j whose knowledge proficiency conforms to a requested domainfor k. ( k ( k ) ) K (tiek) (i, j ) peer KQ(serialized (i, jknowledge ) x K (reputation ( j) where kj) whose k) proficiency conforms to a requested knowledge domain k. y Bloomknowledge taxonomyproficiency matrix representing peer j’s knowledge proficiency K (proficienc ( peer j peer ) is jajwhose whose knowledge proficiency conforms conformsto toaarequested requested knowledge knowledge domain domainwith k.k. where k) proficienc yj ) is a Bloom taxonomy matrix representing a query by peer i that is requesting for KQ ( i , is a Bloom taxonomy matrix representing peer j’s knowledge proficiency with Kproficienc ( j ) ( k ) where (k ) yy a Bloom Bloom taxonomy taxonomy matrix matrix representing representing peer j’s j’s knowledge proficiency with with Kwhere K( k(proficienc )isisisa aBloom taxonomy matrix representing query byknowledge peer i that proficiency is requesting for KQ to a requested knowledge domain k. a peer k) )( k ) (i, (j()j j)respect where peer j whose knowledge proficiency conforms to a requested knowledge domain k. is Bloom taxonomy matrix representing a query by peer i that is requesting for j whose KQ ( i , j ) i a Bloom taxonomy matrix representing a query by peer that is requesting for peer respect to a requested knowledge domain k. (k ) e is a Bloom taxonomy matrix representing a conformance requirement requested K (conformanc ( i ) respect respect to to a a requested requested knowledge knowledge domain domain k. k. peer j whose knowledge proficiency conforms to a requested knowledge domain k. k ) proficienc k.proficiency proficiency conforms to a requestedaapeer knowledge domain conformanc Bloom taxonomy matrix query peerrequirement i that is requesting for KQ (yi,e j )knowledge Bloom taxonomy matrix representing representing j’s by knowledge with K is aaaj whose Bloom taxonomy matrix representing requested Kconformanc ) is (( kk )) ( k ) ((ipeer knowledge proficiency conforms toconformance a requested knowledge domain k. conformanc proficiencye e aa Bloom Bloom taxonomy taxonomy matrix representing apeer apeer conformance conformance requested requested KK( k( k) ) ) isispeer j’sj’sknowledge taxonomy matrixrepresenting representing knowledge proficiency with Bloom taxonomy proficiency with respect to a ((ij)i)by i to peers whose matrix knowledge proficiency conforms to requirement arequirement requested knowledge y peer jBloom whose knowledge proficiency conforms to aj’srequested knowledge domain k. respect to a requested knowledge domain k. is a taxonomy matrix representing peer knowledge proficiency with K (proficienc ( j )by peer i to peers whose knowledge proficiency conforms to a requested knowledge k) k. requested knowledge domain domain k. by by peer peer i i to to peers peers whose whose knowledge knowledge proficiency proficiency conforms conforms to to a a requested requested knowledge knowledge respect to a requested knowledge domain k. proficienc conformanc ey Bloom taxonomy matrix representing peer j’s knowledge proficiency with Bloom taxonomy matrix representing conformance requirement requested taxonomy matrix representing requirement requested by peer i to KK (i(respect )j )isisa aBloom domain k. k) ( k )(serialized to a requested knowledge domain k. a aconformance conformanc e the serialization ofmatrix 6 by 4 Bloomrequirement taxonomy matrix. 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KQ KQ( k( k)()i, j ) ((iis,iby ,j j)a)peer KQ KQ ( ( i , i , j j ) ) ( k ( k ) ) number between 0 and 1 representing the knowledge relationship K i to peers whose knowledge proficiency conforms to a requested knowledge ( k )conformance tie domain k.number is anumber taxonomy matrix representing conformance requirement requested (aia)real (k ) real between 0 and 1proficiency representing the knowledge relationship tiepeer i and KtieK between 0knowledge and representing theaconforms knowledge tieknowledge between peer iBloom to peers whose torelationship a requested (tie k ) (i , j ) isby domain k. is is a a real real number number between between 0 0 and and 1 1 representing representing the the knowledge knowledge relationship relationship tie tie KK( k( k) )(serialized (i,i,j j)) between peer i and peer j with respect to knowledge domain k. k. respect to whose knowledge the serialization of KQ is a 6 conforms by 4 Bloom taxonomy matrix. KQ( k ) (peer idomain , jby ) j iswith ( kdomain ) (i , j ) , which peer i to peers knowledge proficiency to a requested knowledge k. serialized between peer i and peer j with respect to knowledge domain k. reputation the serialization of which is peer a 6 by 4reputation Bloom taxonomy KQ i)between , jis) aisareal KQ ,and j ) ,to number 0 and j’s regardingmatrix. contribution to the real number between 0) (irepresenting 1representing peer j’s regarding K (tie ( (jbetween (respect krespect k) peer peer i iand andbetween peer peer j jwith with to knowledge knowledge domain domain k.k. reputation k )(serialized domain k. reputation is a real number between 0 and 1 representing the knowledge relationship tie is the serialization of , which is a 6 by 4 Bloom taxonomy matrix. K ( i , j ) KQ ( i , j ) KQ ( i , j ) is a real number between regarding Kreputation ( j ) (( kk ))( k ) ( k0 ) and 1representing peer j’s reputation requested knowledge domain 0k. reputation is is a a real real number number between between 0 and and 1representing 1representing peer peer j’s j’s reputation reputation regarding regarding KK( k(tie ( ( j j ) ) is a real number between 0 and 1 representing the knowledge relationship tie ( i , j ) serialized contribution to the requested knowledge domain k. k) ) serialization , which is a 6 domain by 4 Bloom matrix. KQ (i, j ) is the KQrespect ( k ) (i , j )to between peer jof with knowledge k. taxonomy a real peer number 0 knowledge and 1 representing relationship tie K (tiek) (i(,k )j ) iscontribution toi and thebetween requested domain k. the knowledge contribution contribution therequested requested knowledge knowledge domain k.k. domain k. between peertotoi the and peer j with respect todomain knowledge tie reputation isis a a real number between 0respect representing the knowledge relationship (i, (j )j )between real number between 0 and and 1to 1representing peer j’sk. reputation regardingtie KK ( k )( k ) peer i and peer j with knowledge domain reputation 3.2K Examples and Discussions ( j ) is a real number between 0 and 1representing peer j’s reputation regarding k) reputation 3.2K (Examples and Discussions between i and peer j with to knowledge domain to the requested knowledge domain k. peer is aDiscussions realpeer number between 0 respect and 1representing j’s k. reputation regarding ( j )contribution ( ) k 3.2 3.2Examples Examples and and Discussions contribution to the requested knowledge domain k. between 0 and 1representing peer j’s reputation regarding K (reputation ( contribution j ) is a real tonumber k) the requested knowledge domain k. 3.2 Examples and Discussions contribution to the requested knowledge domain k. 3.2 Examples and Discussions 3.2 Examples and Discussions

3.2 Examples and Discussions

The value of K (tiek) (i, j ) indicates the degree of peer j’s knowledge matches peer i’s query: the eit ehtof :yrK eu(tiekq) (si’, ijr)eeindicates p sehctam delwonofk peer s’j rej’s ep knowledge fo eerged ehmatches t setacidpeer ni ) ji’s eulav ehT ,i( query: The value theegdegree ) k ( K fothe higher the value, the stronger the tie. 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For example, consider a peer, Steve, who requests peers ( SE ) c conformance s a d e t o n e d e b n a c t s e u q e r s ’ e v e t S ” . g n i r e e n i g n E e r a w t f o S “ f o egdelwonk knowledge . of Steve’s request can be denoted as . )ev“Software etS ( namro)fnEoSEngineering.” K K ( Steve ) ( ( SE ) who have “Software Engineering” with the of applying conceptual . Examples and Discussions higher the proficiency value, theinstronger tie. Based on theknowledge aforementioned and example shown Figure 1the and 2,For we example, found twoconsider a peer, Steve, who the degree of peer j’s knowledge matches peerequations i’s query: the the tie Theonvalue degree matches query: i,wj ),2indicates k f) (e owof tofd“Software nKu(opeer dnequations a 1 eruthe gimatches Fand ni nthe wrequest oof hspeer eli’s pcan mj’s ashown xknowledge e edenoted hthe t dinnaFigure snas oiKtaconformanc e peer dee2, owe ii’s tne)found m athe eht no desaB Based the aforementioned example 1uqand of K (tiek) (i, j ) indicates the degree of j’sEngineering.” knowledge peer query: knowledge Steve’s be . eroftwo (nSteve ( tie SE ) who have knowledge “Software Engineering” with the proficiency of ap and peers, Albert and Chris, whose knowledge relationship ties, K Steve Albert ( , ) . For example, consider a peer, Steve,tiewho requests for peers (tie SE ) eittie. higher the value, For consider adepeer, who requests The value of)and the matches peer The value of the degree peer j’s matches peer i’s query: query: (i,ejv)eindicates dnpeer aaforementioned ,sknowledge eidegree texample, pexample ii’s hsof nof oipeer trelationship ashown lerj’s eknowledge gknowledge lwoties, nSteve, k1 and eK so2, ,sirh,i’s C dfor na)peers tthe rthe eblhigher A ,sreep and peers, Albert Chris, whose degree j’s knowledge peer query: the tthe rKeb(consider l)A tindicates Sequations (the j ) indicates Albert kstronger )peer, Ematches S ( K Steve, ) (i, the (h SEw ) ( Steve Based onof the and thewho in“Software Figure we found two ue, stronger the the tie. For example, a requests for peers knowledge of Engineering.” Steve’s request can be denoted as tie Engineering” withthe the proficiency of applying conceptual Steve, stronger the tie.than ForEngineering” example, consider athepeer, who for who have zero, which means both Albert and Chris conform topeers Steve’s K ( SEvalue, ( Stevethe , Chris ) are greater who knowledge “Software with proficiency of requests applying conceptual tie eit tie ) have t stronger tie. acesipeer, Steve, requests peers Chris, knowledge relationship ties, Steve stronger the tie. For example, owledge “Software Engineering” the of applying s,value, ’Chris vreetand Sp)the owith tehm ft n:oyconformanc drkenFor rsexample, lproficiency neconceptual aboth eofm hkAlbert h’tajwcrieK,deand o(nconceptual rie)fz()oChris ne,aieh(who tge,kiAlbert r(conform edtknowledge aefehort)gesuand elrtaatovcfor ) s i r h C than which means Steve’s K SE ehigher hpeers, ethe uqAlbert ieconsider sare caergreater toahpeer, m ecrproficiency gwhose eSteve, dsuthe eiqrlhwsC nwho easp’tthe jrequests reebheBased cpAtafhm otoefor ebeon gconsider rsdgpeers l d w e o h n t s p r e i e d h n T i eulin avFigure ehT 1 an j K ) j ,e,iv(e)ektitS( K(shown e’oizero, ) EfSo( K ( tSE:)y(rSteve ) Engineering”with knowledge“Software applying of“Software the aforementioned equations and the example ing.” Steve’s request can be denoted as K ( SE ) ( Steve) . conformanc e tie knowledge of “Software Engineering.” Steve’s request can be denoted as . K ( Steve ) request in terms of knowledge relationships. Moreover, since is greater ( , ) K Steve Chris conformanc e tie who have knowledge “Software Engineering” with the proficiency of applying conceptual ( SE ) ( SE ) ge“Software “SoftwareEngineering.” Engineering” with the proficiency ofdenoted conceptual Engineering.”Steve’s eitapplying zero, which means to request Based the )a sreKe(pSE r) (oSteve f stSteve’s uaqeregr )of orequest hi wknowledge s,rierevheeC pcan tS,rcan o,vrfebe setSbe tprelationships. s(eadenoted drisoenhcow cas pre(eSE m tSvboth ,xreereoAlbert rpM o(FaSteve r.since dtii)hse.ns.hChris gaenl)conform rtaeswhose xgeedh,eChris rtaforementioned ,eFSteve’s uo.lnknowledge ea)kivt is efhotgreater oithrrelationship tnsi ehtste,uequelar v eties, ht rehgKih(tieSE ) ( rsterms ee,tChris sare npeers, is,eKvl,Moreover, oAlbert seand piand notChris, ocrie(tie t,SE ep(oon rm low sremghrngeequations )greater ethan K request in K Steve )u Er Sqe (e ons and the example shown in Figure 1 and 2, we found two conformance conformanc e tie and theonexample shown inChris Figure  and , we found two peers, and knowledge Based aforementioned equations and the example shown in Albert Figure 1 and wewhose found two knowledge of “Software Engineering.” Steve’s request can be denoted asChris, K2, (Steve’s Steve ). tie ware Engineering.” request in than K (the ( SE )fw in knowledge Moreover, ,“Chris K aforementioned equations the example we two larequest utpSteve’s ec(tieSEno)and cSteve gterms niy, lAlbert pof pacan fl)oa,ube yshown tpcendenoted cenicois irelationships. cfin omore rgpFigure nias yehlpknowledgeable tKp(ha1SEtiand otie”y2, gc(nSteve ierieceifound fsince nthan o)irg.pnEeAlbert he(tSEra)h(w tSteve itw foS”terms gneirged)eof enlisw igmeeting ogreater nnE)k eervaaw htfooSh“w egeditelwonk evah ohw tie s ’ e v e t S g n i t e e m f o s m r e t n i t r e b l A n a h t e l b a e g d e l w o n k e r o m s i s i r h C , ht , Chris is more knowledgeable than Albert in terms of meeting Steve’s than t r e b l A e v e t ) , ( ) ESboth K Steve Albert ( , ) are greater than zero, which means and Chris relationship ties, K ( SE ) ( Steve, Albert ) and than zero, whichSmeans and K ( SE ) ( Steve, Chris ) a re greater e knowledge relationship ties, ( K naAlbert ( SE ) peers, Albert Chris, whose relationship ties, K (tieSE1 )detail Albert ( Steve ,we ) and tie tieecnexample amrofnaforementioned ocandshown eknowledge cnamro1 fnoand cknowledge Based on the equations and the example shown in Figure and 2, found two entioned equations and the in Figure 2, we found two request of applying conceptual of “Software Engineering.” The calculation of in Albert and Chris, whose ties, Albert sarelationship d)conform bmore naEcSknowledgeable tsK esurequest a( SE qed) r(eSteve tso’nein evde,than tterms Seb”Albert .ngan)of cirand etknowledge seenuterms igqnerE esiof e’eravmeeting ewttSfo”S.“gSteve’s nfioreegnMoreover, digenlw Eoenrkawsince tfoS“ fo egdelwonk . )than .e,)tChris eveAlbert tK S ((knowledge K eovneetdS (iseto SE ) ( Steve Steve’s both andS (,Chris relationships. (K fpo s)nEand lum cleagcdconform leialw teodnknowledge ekhsto T’j )tie ”Steve’s erequest grgneEd eerhin atwsterms teEngineering.” ftoaSc“idfnoof d,ie(l)wktThe onkfodetail laeurelationships. cnehoT c gniylpMoreover, pa fo tseuqsince er ofreeapplying conceptual of ht :yrequest reutie q s’iAlbert eohictataChris r.egenpirfeoof enei“Software i e)gjknowledge utlpaevcalculation K (tieSE ) ( Steve zero, whichemeans both ( K tie Albert and Chris, whose knowledge relationship ties, and K Steve Albert ( , ) relationship tie is illustrated as follows: Chris Albert are greater than zero, which means both Albert and Chris conform to Steve’s Kpeers, ( Steve , Chris ) is greater than , is more knowledgeable than in terms and Chris, whose knowledge knowledge relationship ties, K Steve Albert ( , ) ( SE ) request of detail calculation of ( SE SE ow t )dzero, nuoof f which eapplying w ,2 dmeans naconceptual 1 oetie w rutgisdiF nillustrated uknowledge noifnew w(oand ,h2)sas deChris nlpfollows: a“Software m1aexconform reugehiFt Engineering.” dnni ato n:w ssnSteve’s oohoisltaloeuflqpsThe em than both Albert hris ) are greater knowledge w adradtexseneteoaheirttthnst,ueedm lulnlieaasrviosneefaihottieptrhaieuthhqsngneoiohdditeeansloaeB ritnegem deelrwoofankeht no desaB tie tie ,ev eepof roknowledge fmeeting stsesince uqerelationship rSteve’s o h w e t S , r e e p a r e d i s n o c , e l p m a x e r o F . e i t e h t r e g n o Engineering.”The request of applying conceptual knowledge of“Software detail , Chris is more knowledgeable than Albert in terms than K Steve Albert ( , ) tie lationships. srMoreover, is greater ( , ) K Steve Chris relationship is illustrated as follows: tie SE ) )) are it tie eit means togreater Steve’s ,,Chris request terms of relationships. Steve Chris re greater than zero, which Albert Chris to SE dK na(relationships. a ,s)etsince iand ih,K nvtieeoisticonform lwhich gMoreover, d,se(eas lSteve’s iwt)ofollows: nis pboth kihgreater snesince sAlbert ooithaw leK r and ,(sSEeirg) h(dChris C elwdo,nconform ak terse)obhis lA w ,,ssrierehpC dna treblA ,sreep einblmeans A eve( SE tSboth Kngreater rtebthan lpA eszero, St(aillustrated )) (trSteve ( ) EeSknowledge (Steve (d )eErS ( Ke rms of knowledge Moreover, calculation of knowledge relationship tie , Chris lautpecnoc gniylppa fo ycneiciforp eht h( tSEiw ”gnireenignE erawtfoT S“ egdelwonk evah ohw ) 0request 0 0of 0applying 0 0ºTconceptual knowledge of “Software Engineering.” The de tie more knowledgeable than Steve’s tie Albert in terms of meeting tie relationships. Tª request in terms of knowledge Chris terms K knowledge )relationships. Moreover, ,knowledgeable )t0Sdis ce natm roo fn)to(cSteve (e SEr S( SE m rosafn, oAlbert cetsoinrhesince s)dC’,ethan dveebntaSK tAlbert oar( eSE tisbm A rSteve ouhfin nqtooebterms cChris sssnºª«i’r0aehevC m hMoreover, nc”a0i.greater hgtw rne0ibr,than oelA re0Steve’s enTzhigtsince nAlbert o0nabªº»Ehst nerareK etin am h)go(cSSteve ei“hrawfof ehgzC n,le)aw ttnSrSteve’s ekgreater g era ) sirhC ,evetS ( ) EeSit( K )o,s,oChris iermeeting vheis (t)aEeeSitr( K ) lt(more d n c s e r e a w t f d e o evethan tsS’ise( vemore K knowledgeable of meeting ve, Albert ) , .Chris 0º0 0 ) ES ( ª0 00 0 01 00relationship tie is illustrated as follows: conformance ( Steve) T «of»«0knowledge Let Steve’s request The be Kdetail wledge of “Software Engineering.” » 0«» T tie 0 0 1 0 1 0 (conformanc SEe)it calculation e i t namof rofnomeeting c e T 0 0 1 0 0 0 request of applying conceptual knowledge of “Software Engineering.” , Chris is more knowledgeable than Albert in than K Steve Albert ( , ) »«ere0othSM conformanc is Steve’s ert ) , Chris Steve’s rnknowledge euLet taoknowledgeable ef rSteve’s tSE aE)Engineering.” nterms r)C eaof v«,The p0calculation eongeodrioet)nM lew neSskpr(oiehfcfdetail lK eotrdeneebsigatdsB tSteve’s eseluw uqqoeof ernrks’efvoetsSmterL et ni tseuqer )drequest )iessli)prT,hm sof ethan vuegtK SrFe((Albert Let eoThe t.terms Steve’s request owmore t dLet egw( SEs,i)2request nirah“Software 1C e,be rbe iK iSe) (nrKgweoin hic(sSteve xeoevemeeting t( d)0En.Ssa( K sihneso0cnitnoaiust»0aqle,erer0dv«»eof evm asoneohs)im ttSan(rcalculation )n (esSteve be E (SE plying conceptual detail « » 0 0 0 0 «0 »«00 0 » ted as follows: 0 0 0 0 0 “Software 0 0 0 Engineering.” knowledge relationship tie is illustrated as« follows: eit »«¬dof »0 0«»¼ of applying conceptual conceptual ationship tieknowledge as dis naillustrated ,srsee’itevneiptSithresgbnnknowledge ietaenlm erlegw oof n)lw t’reevbeltAof vneitfollows: )srequest S ,eg“Software eSe(m o Engineering.” sm loiAtThe aehrdetail tfo¬ee0lsgb¼¬m a00calculation td0oenlniw e0hr0¼nowamh0ts¬,¼sieilrsbhiarCehgCdde, The klrAeeb,reloA hC0na,hof taroenbtdetail vm ecalculation tS,ss(ir)eEªseSe0iit(rpK )0ttreb0lA ,0eve0tSº T( ) EeSit( K naht 0k 0toren0be0kslA ) ES (fK proficiency « then Let knowledge proficiency, ( Albert ) be the matrix shown in Figure 2(a), K knowledge relationship tie is illustrated hip tie is illustrated asAlbert’s follows: (proficienc SEas ) y follows: proficienc eit 0 0eqcenr1oc 0gni0ylpp0a»» fo tseuqer ycshown neiciforp in y Ta Let Albert’s knowledge proficiency, be the matrix Figure 2(a), then K (ahw Albert ).g conformanc ee T2(a), Albert’s Let knowledge proficiency, be the matrix shown in Figure (a), then s ’ e v e t S o t m r o f n o c s i r h C d n a t r e b l A h t o b n e m c i h w , o r e z n a h t r e t a e r g e r a ) s i r h C , e v e t S ( K f o n o i t a l u c l c l i a t e d e h f T o ” n . o g i n t a i r l u e e c n l a i c g n l E i t e d a e h t f T o S ” “ f n o i r e e g e d n e i g l w n E o n e k r a l a w u t t f p o e S c “ n f o o c g g n d i e y l w p o a n k f o l a t s u e t p u (s SE )ax « , y c n e i c i f o r p e g d e w o nk s’treblA teL n e h t , ) a ( 2 e r u g i F n i n w o h s i r t m e h t e b Let Albert’s knowledge proficiency, be the matrix shown in Figure then ) ( t r e b l A K ( K Albert ) E S ( ( ) K Steve Let Steve’s request be T ) E S ( ( SE ) 0 0 0 0 0 0 ª º ( SE ) ª0 0 0 0 0 0 º «0 0 0 0 0 0 » «0 0 1 0 0 0» ª0 0 0eit 0 0 0º T :ishwsnoolliotaf lsear deegtadret»lsw ulli s:isewitoplliohfssnaoidtaelteartesugldlei lswi oenitkpihsnoitaler egdelwonk eproficienc cnis e,»yrevoeroTM «.0spconformanc 0 1 e 0 0 T 0» Tonk fo smret ni tseu«¬q0er 0 0 0 0 0»¼ eve) T « retaerg si ) sirh»C ,«e0vet0S ( ) E1S ( K0conformanc T « proficienc y conformanc e T 0 0 0 ( Steve ª)0( SE ) 0( Steve KQ ()Steve ( Albert Steve 0 be))0 KK0(KSE(ºSE(proficienc ªSE e 0 0 0Let 00»request Albert (»(Albert )) x )Kx( SEK )n0amrof0n)oTc 0º (Steve’s SE(0 )0 )SE «, ,0Albert ) ) T 0 c0 conformanc (KQ )(TSteve Steve quest be«0K (conformanc 0 0»x» ) treblA ( ycneici)foErSp( K eiproficienc t ) treyblA ,evetS ( )oe)nv0ket0eS1re(o(eSteve )0ni t0rKe»b( SEl0A) n0a»yh(tAlbert )d x e«K«0lw «)e(0eSteve « SEs’) evetKQ »0fo«,01Albert S (QK (n SE ( SE S g i t m s m r e t e l b a e g m si)0E)sSi(rKh0»C , ) tproficiency, naht( Albert ) be) Ethe reblA ,eTvetS (K 0 0 0 0 0 0 ) E(SSE (K Let Albert’s knowledge matrix shown in « conformanc e T 0 0 0 0 0 0 T T ) conformanc e T T « » ¬ ¼ « » ( ) K Steve Let Steve’s request be ( Steve) e K ( SE ) « 0.9.0)9 0T0.08»º.80 0.40.400.40¬.04«000 00º0 0ª000ªº 0ºª000 0000 000¼0»0 00º 0 0ªºT ªª(0SE 0 0 0 0 «0 0 ¬ 0 0 0 0«r»0ee.9nigº0n0¼».E8 e0r0a.w 0ª»«0o0nk100la04u0.t0p»0ec04n0.o»0c0 g8n0.i0yº«» lp9p.a0ªfo tseuqer 400tf.o10S0.“4«0 f0o00e» g0d0ª«0eº«»lw y fo noitaluclac liated ehT ”.gni«ª 0»0.3.3 0.032(a), shown in«Figure ( Albert ) be the K (proficienc .30 0.20Kthen .2proficienc 0 0.1y0( Albert 0»0x 0«« »0« )º»be 0 matrix 01 10 o»fn0oc 0»«in Figure « matrix SE ) T = Let Albert’s knowledge proficiency, » » « «»000T )the 0 0y ( Albert 0 0=) be 0« ¼0the ¬0in0Figure x2(a), e00v0then et01S0.1(0¼e0cna0m20rshown K s(’eecnvaemthen troS)fnEotSce( K L eeb tseuqTer s’evetS teL (0 SE ) 01.1 0 0 0 0 .»0i) Eh0Ssproficienc 3o0.0i»«etablyet3sr.e)0eeu««gvqdeeetrS2(a), . 3 0 . 3 0 . 2 0 0 0 (n » « nowledge proficiency, ¬K0(proficienc matrix shown 0 . 6 0 . 5 0 . 3 0 . 2 0 0 0 conformanc : s w o l l o f s a d e t a r t s u l l i s i e i t p l w o n k SE ) « » » » « » « » « 0 . 6 0 . 5 0 . 3 0 . 2 0 0 0 0 0 0 0 0 0 0 0 KQ Steve Albert ( , ) K ( Albert ) x K ( Steve) x = x = « ( SE ) SE SE ( ) ( ) » « » y00 00 proficienc y « « » « » « «0.6 shown » « » 0 0 2 . 0 3 . 0 5 . 0 6 . 0 .30(proficienc 0 . 2 0 0 0 0 0 0 0»00».5 00K 0 0 0 0 0 0 0 0 Albert’s knowledge proficiency, be the matrix shown in Figure 2(a), then ( Albert ) ¬ ¼ ¬ ¼ dge proficiency, KLet be the matrix in Figure 2(a), then ( ) Albert 0 ) 00 0 0« »¬¼ » «¼¬0 0 0 0 0 0»¬¼ ( SE ) « «¬ 0 » 0¼0 00 SE e Albert ) x K (conformanc ( Steve) T 0 0 0 00 00¬ ¼e ¼0¬0 00T 00 0 0 ª00.90 ¼ 0.80 ¬0.4 0.4 0 0º ª0 0 0 0 0 0º T 0 0 proficienc y ¼ ¬ SE ) 0 0 0 0 0 ª T( SE ) T ( Albert ) x000K º (conformanc KQ )e K ( Steve ) proficienc y ( SE ) ( Steve, Albert conformanc y c n e i c i f o r p y c n e i c i f o r p 00eeruhgt0ieFb0n) it»rne0w Albert ) K ( SE ) f0ortepL0e»gde«l0wo0nk s1’tre0blA0teL0» n(eAlbert ht ,)a()2xeKru( SE gi)F ni n(wSteve oª0ehºhs0t)x0,i)ra0t(a2m eh,tyecbne) tirceifbo«lr0Ap.(3egd0e.)3lEwS (oK0n.k2,ysc’nt0re.e1ibcliA bSEªºlo)Ah(s xirt)aEm «n S(K T 00 00 0.20 0 00 00 0 »x« » « » « º0T 0 =0 « 0 T0 0ª » ª º«» proficienc y conformanc e = proficienc y conformanc e T 0 . 4 0 . 4 0 0 0 0 0 0 0 0 º ª º 0 0 0 . 2 0 0 0 0 1 0 0 KQ) (xSEK) (( SE Steve , Albert ( Albert )00x.»4KT (»«SE0 ) T0º (ªSteve ) K ( SE ) ( Albert ( Steve) ) ««ª0K0»(.SE e» c0 n)amro0fnoc 0 «0 ) 00 9 . 8 0 . 4 0 0 0 0 0 º ) » « » « 0 . 6 0 . 5 0 . 3 0 . 2 0 0 0 0 0 0 0 0 4 6 ) ( e v e t S K e b t s e u q e r s ’ e v e t S t e L = 0ES ( 0 « 2.0 » 0 0« 00 00.20 00 00»º 0§»« 0.2 0.1 ª00.9After 0»»0.8««serialization, 0 00.4 10.40 0KQ 0 0serialized 0º=»» ª««0000(»Steve » ( k«)0(m,»0n0) ·¸1)= » « » 0 0 0 0 0 0 0 0 = 0.2 , Albert ) KQ ¨ = 0 0 0 0 0 ¦ ¦ 0 . 3 0 . 3 0 . 2 0 . 1 0 0 0 0 0 ( ) k T ¬ ¼ «0.3 0x.3 0.2 0.1 0 0» ««0 »0.90 01.08T0 00T.04 00ec».n4a0m»«rof0noc 0» « 0T »0 y0cneeiccinf0oarmp¬rof0n00oc 0» 00 0« 0 ycn0eiciforp0 0¼ ¬0 0 0 0 0 0¼ ª º ª º m n 1 1 t r e b l A e v e t S Q K t r e b l A e v e t S Q K ) , ( ) , ( ) e v e t S ( K x ) t r e b ) e l A v e ( t S ( K K x ) t r e b l A ( K © ¹ x = 0 . 9 0 . 8 0 . 4 0 . 4 0 0 0 0 0 0 0 0 ª º ª º 0.3 0.= 2 «0 0» «0 0 0 0 0 0»»x «¬0¼0 0 0 00 0 0 » 04 ¬¼ ) ES6( » « ) ES ( ) ES ( ) ES ( ) ES ( « ««0.6 0.5 0».3 0.2 »40§ § 06 » «0 »0 0· · 0 0 0»» «0.3 0«.03.6After » 0«KQ ».02.5« serialization, »serialized serialized » « » 0 0 0 0 0 0 0 . 3 0 . 2 0 0 0 0 0 0 0 0 0 0 0 0 . 3 0 . 3 0 . 2 . 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 . 1 0 0 0 0 1 0 0 0 = = 0.2 ( Steve , Albert ) KQ ( m , n ) ¨ ¸ ª º ¼ ¬ ¬ ¼ »KQ 0 0 0 0¼After 0 0 0» 0« KQ ¦ ( k¼) «« ( Steve, Albert T 0 ¬0 serialization, »0 0ª T)0º=»0¦ yc¦ ne0ic¨ ifo¦ r0 p » x«( k«0()k ) (0m, n0) ¸ =0 0.20 0»» 0 = « « .0 teL9.»0ª t.3,0)a(02.2e0rug0iF00n»i xn0w o0ºh»0s0(=kx)«i0¬r00«ta00m .0600eh000t.e5b000)reputation 0tr.0e3blA0¼(.2m m14 ©10§©n) En16S4(01¼.K0» 0¬,y«40c.0nei0c08iªf.¹0o0·¹rpº 90e.g0«dª00elw400o¼.0»nk0.s42’.t0re0b8lA «0.6 0¬.50neh0After « » 0 0» « 0 serialized ¼ ¬ « » « « » » serialization, = 0. « serialization, = = 0.2 KQ ( Steve , Albert ) KQ ( m , n ) ¨ ¸ = 0.8 Albert’s knowledge reputation be ( ) K Albert ¦ ¦ = ( k ) k ( ) « » « » 0 « 0 0º LetAfter 0 0 0 1 0 0 0 0 1 0 . 0 1 2 . 0 0 3 . 0 0 3 . 0 0 1 . 0 2 . 0 3 . 0 3.0 » « » ª0 0 0 00 (»SE0) « 0»0 0º 0 0 ¬ 0 »ª00 0 0 0 0 0 00 00¼ º ¬0 0 0¬ 0 0 0¼ x m 1 © n 1 ¼ ¬0 0 « 0x¹ » 0 ««00 =00¼ 0 0 0 0» « = 2 0 0 0» »0serialized »0 02.0 0 3.0 05«.0 »06.0««0 2.0 3.0 5.08 6.»0« 0«0 00 00.20 reputation tie reputation 00««mr)0ofxn»»o0cK 0Albert Steve , Albert )» KQ ( Steve , Albert (=Albert «0 KLet 0 0 0 0¼ « T e cnareputation ycnei)cifo= r«p 0».2 x¬0 k ) (Albert’s kreputation (Let (reputation ) « (»SE ) knowledge be ( K «0.8 0= 0.16 » Albert’s knowledge be 0. 0 0 . 2 0 0 0 0 0 0 0 0 0 SE ( ) Let Albert’s knowledge reputation be ( )l0= K ª º0x ) tr0e0)b= ) e v e t S ( K A0.8 (0.8 = ) ) ES0( Albert 0 ª00 =00»« 0 0 0 0º 00 0) E0¬S ( K¼00 )¬0trebl0A ,eve0tS ( ) E0S (QK 0 ¬ » ¼0 0«0 00 0 0 0 00( SE ¬ ¼ » 0 0 « 0 0.2 0reputation » « » 8 0Let 0knowledge 0» tie serialized 0 0Albert’s 0 00,»»Albert 0.8 ) = 0.2 x 0.8 = 0.16 reputation be, Albert (0»Albert ) (=Albert K ( SE ) 0) x K ««0 ( Steve serialized reputation 0 00 00¼«00.2K T ) »reputation KQ = k SE ( ) ( ) 4 0.02ªxº00.8 0= 0.16 K( k ()tiek()Steve ( Steve ,8Albert ) KQ ( Steve , Albert ) x K ( 8Albert ).0= 8 0 0 0 0 0 0 = « « » k )0 (¬ ( SE ) .0 0 0 0 0 0 4 . 0 . 0 9 0 0 0 0 0 0 0 0 0 ª º ª º º ª ¼ «0relationship 0 0 00Steve 0¼knowledge 0 0 0tie0to Albert 0» reputation «0 0 ¬0 0As0 a0tieresult, » 0 ’s is 0.16. « , »Albert »0 ) 0 KQ0 serialized K Steve», Albert ( Steve ) x1Albert K ( Albert 0 0.. 0 2.0 0 0«« 0 0 0 0 .0»(»»SE0 )2.0is 30.0 2.30).0=««0 0.02««x»»0.8 0= 0.16 « ( k )a( result, ( k )«1 relationship Steve’s As knowledge tie to 0 0 0 0 0 0 » « » 0 0 0 0 0 0 ¬ ¼ x = = = ¬ ¼ »’s0 knowledge 0 to2.Albert 0»0 3.00is 0.16. 50.0 06.0«0 0« »0 0 0 0 0 0« 0 0 0 relationship 0 0« »0 tie As Asa aresult, result,Steve Steve ’s knowledge relationship tie to Albert is 0.16. « » « proficienc » » «» « y LetLet Chris ’s knowledge matrix 2(b), Chris’s knowledge proficiency, be the 0 0 0 0K 0 ¼0) be 00the00matrix 0 0 ¬shown 0shown 0¬ ¼in 0inFigure 0Figure 0 (b), 0 then 0then 0¬ query of ¬ ( SE¼0) 0 (Chris ¼0 0 proficiency, As a result, Steve’s knowledge relationship tie to Albert is 0.16. Steve’s request is denoted as: calculating the degree the of Albert’s proficiency conforms to conforms proficienc y0y 0 the query of’scalculating degree of Albert ’s8ºproficiency Steve’sinrequest is denoted 0 matrix 0 0ªtoshown Let the (Chris )0be ) ) LetChris Chris’sknowledge knowledgeproficiency, proficiency,KK( SE(proficienc be shown inFigure Figure2(b), 2(b),then then8 ( Chris ) SE »0 0 0 2.0the 0matrix « 8 0 as: proficienc y » « Let Chrisof’s knowledge proficiency, the matrixto=to shown Figureis2(b), then 8 KAlbert (Chris ) be conforms the Steve ’sin ( SE ) ’s’sproficiency thequery query ofcalculating calculatingthe thedegree degreeofofAlbert Steve ’srequest request isdenoted denoted »0 proficiency 0 0 0 8conforms 0 0« » « as: the degree of Albertconformanc as: query of calculating the 0e 0 0 Tconforms 0 0¬ to Steve’s request is denoted ¼’s0 proficiency proficiency KQ8( SE ) ( Steve, Chris ) K8( SE ) (Chris ) x K ( SE ) ( Steve) as: T

conformanc 2SEproficienc 0.7y (yChris 0.7 ) x0) .xK 7K(conformanc 0.8 e0(e.Steve 8( Steve ªK0K.(proficienc º ª)0T) T 0 0 0 0 0º SE ) ) ) (Chris «0.8( SE )0.7 1 0.8 ( SE 0.7 0e.7»» ««0 T 0 1 0 0 0»» T y conformanc KQ( SE ) ( Steve, Chris )= « K0.(proficienc ( Chris ) x K ( Steve T x ) SE ) ( SE ) «0ª .ª202.200.2.07.700.3.07.700.2.07.7 00.08.8 00.08».º8º«0ª0ª000 000 000 000 000» º0º » « » «

KQ , Chris ( SE ) ()Steve KQ ( Steve , Chris) ) ( SE

Let Chris’s knowledge proficiency, K ( SE ) (Chris ) be the matrix shown in Figure 2(b), then eit roshf’nteoovbcetssSnira”he.C m riegb,nolE A rezhetrnoaabw hsttfnroaeSet“amerfhgoceiehrgawd)e,sloiwrehozC g era ) sirhC ,evetS ( ) Ee derotofnneodc seibrhsC n’eadvcenttaSsetoruetqbm elrA gndhirnceaiehtnw nkn,eavhettSre(t)aEeS r( K . )evetS ( ecnamsr’oe)fnEvoSec( tKS soat m proficienc y Let Chris’s knowledge proficiency, K ( SEproficienc matrix shown in Figure 2(b), then (Chris ) be the ) ’s proficiency y theLet query of’scalculating theproficiency, degree of Albert conforms to shown Steve’sin request is 2(b), denoted the imatrix then eit Chris )eistbe proficienc rChris eta,2er’s gdnaknowledge sknowledge i1)esriruhgC icK nK ehvt,yeod(ve(nChris gedreoerM .neshkptFigure iFigure hfnsoonodsim raleeBtr neigthen dtseelw uqoenrk fo smret ni tse ier),h)erC eraotSM owt dnuLet of eChris w iF,envproficiency, ietnSrwe( ot) aEhSes(rKgelpesm a)i(xs(SESE s(n)oE).Sibe t( apKuihthe qesencndomatrix estnaol,eritrenveeoshown m olw faoin etsa2(b), the query of calculating the degree of Albert’s proficiency conforms to Steve’s request is denoted as: eit the calculating the of Albert ’s proficiency conforms todnSteve Steve’sbrequest request isepdenoted denoted dna ) tas: h’degree ebnrliA low naekrgetdensliw othorw hhCtsieto rthe eblquery Aquery Sof(gcalculating Albert s’,evetof nSi(tK eem,sfeoit sthe mprsiedegree tesvneoitiStraeglof teengm adhetf’s elproficiency sobm enconforms bklAe,srnioram lsbiarahegCdte,r’s)lew hCna,h)ttreblA ,evetS ( ) EeSit( K troleAbnlkA,es,reroveism tSs(i) EseSiit(rK )E as: as: ero Terg s’evetSKQ ot mfor(oSteve atetdK reeproficienc bhlfA itehrdwaw e(SSteve z”“.ngfanin elw rpoheSCc“n,efovoNetworks nfnooitca,lsChris uirchlaCThe c)dlinaIdentification To h”nt.oygo(of inbChris taiCommunities rsluenecanle)aim cgx nK lhE ica(conformanc e,hotfT ohi)rteWeb eedntaeig.0 nrgE othrough neerkraala)wsutitfSocial ceetgSgn(di)yeEelSiw pt( K poankfolatsuetpueqcenr oc gniylppa fo tse SE ) of Practice ( SE ) ( SE ) proficiency conformance ollof)sTaeldweotanrktsufloli ss:im sew ( Steve retaergKQ si ( SE c(.nSE iproficienc s) 0,r.e7yvy(Chris oChris e0r.o7M))x0x..K s7K p( SE iconformanc h0)s.n8oi:tsae0w rieto0tplliTnohifsnstaoseidtuaeqlteaerrtesgudllei lswi oenitkpihsnoitaler egdelw ) s)i(r(Steve hSteve C ,ev, Chris e, Chris tS ( ) Ee)Si)t( K ªeK0K proficienc conformanc eler egdT 0 0 0 0 2 . 8 KQ ( ( Steve ) T 0 ª º º SE SE ( SE ) ( ) ( ) KQ( SE ) ( Steve, Chris ) K (Chris ) x K ( Steve) «0.(8SE ) 0.7 1 0.8 ( SE0).7 0.7» «0 0 1 0 0 0» T eit 7on0k.8erom 0.8»sºi «sªir0hC0, ) t0rebl0A ,e0vet0S»º( ) ETS ( K naht s’evetS gniteem fo smret ni=tre«bªl0A.2nah0t.7elbT 0a.e7gde0l.w xT T ««0ªª0.002..8.22 00.002..7.77 0º.00130..77 00.002..8.707 000..7.88 0000..7.»088»ªºº ««0ºªª000 0000 0100 0000 0000 00»00»ªºº fo noitaluclac liated ehT ”.gn=ir«e««e«0n0.i.88gnE00..e77ra»w01t1foS0“00.f.808o e0g10.d.77el0w00o..»70n7»«»k»x«l«a»««u00tpe00cno101c g01n0iy0l0ppa»00»«»f»o tseuqer ««0.2 0.2 0.3 0.2 00 00¼»«»» ¬x«0»««0T 00 00 ec0n0amro0fn0oc 00¼»«»» T ==¬«««0.2 0.2 »0.3 0.2 0 K eb tse)euvqeetrSs(’eecnvaemtroS)fnEotSce( K L eb tseuqer s’evetS x )evetS ( :3sw o00l.l20of s00a0 de0ta00r00t»s«»»ull«»«i«000si e000it p00i0hsn000oi)t0Ea00Sl(er000e»«»»gdelwonk » « 0 0 0 . 2 0 . 2 0 . 0 0 0 ¼ ¬ ¼ ¬ » « » « ª0« 00 00 0» 00 0º0 0 0 «» »« 0 0 0 0 0 0«» «0¬¬ 00 10 0¼000 000»0 00 0 00¬¼¼ ¼¬¬0 0 0 0 0 0¬¼¼ ª0 0 0 0 0 0 º = «T 0 0 0 0 0 »0 p rp fotrepLegdelwonk s’treblA neht ,)a(2 erugiF n«i«0ºªª0n0w0on00hehs0t1x0,i)r0at0(a02m0e0er0uhg0t 0i»0eF»bªººn) tirnew eh,tyecbne) itcreifbolrAp(eycgndeicei)floEw bloAh(sycxneiircti)faoErm Knk,ysc’ntreeibcliA S(K S (o » « » « = « »« 0 0 1 0 0 »0«» «0«00 000 0001 0010 000 000»» ==¬«»««0 0 0 0 0 ¼0»«»» T )evetS ( ecnamro)fnEoSc( K eb tseuqer s’evetS teL ¬»»«««00 00 00 00 T00 00¼««»»» ecnamrofnoc 0 0 0 0 )0eve0tS ( K x ) treTb)elAve(tyScn(eiecci)nfoEarmSp(roK)fnEoSc( K)xtr)etbrelAbl,Aev(eytcnSe(ici))fEoErSSp((Q KK ) treblA ,evetS ( ) ES ( ¼¬¬0 0 0 0 0 04¬¼¼ 6 ) ES ( § · serialized T 1.0 9.0ª0 4.0 4.0 8.0 9.0ª After serialization, ) = cªneiTc¨ºif¦ orp KQ ( k0 )n 0ht e0b, Chris .(0m 0,cni4f).o0¸r= c e i p 08eªg.0deºl0w onk s’treblA teL neht ,)a(2 erugiF ni nwoKQ hs (xk i) rtºa0m(eSteve ) t0reb0lA (0y¦ 4 )0 6( K0 ,y4 E S 1 1 m n © ¹ § · «.0 »03.0«0 1.0 2.0 3.0 3.0« » After ,2n.0) ¸·=031.0 , Chris ) =0«¦44»¨0§¦66 0KQ10( k.)0(m Afterserialization, serialization, KQ(serialized 0 1 0 1 kserialized ) 0 (0Steve « = » After serialization, serialization, KQ KQserialized Chris ) =«mx¦ ( Steve,,Chris 1»©§¨n¦ 1 KQ( k ) ( m, n)¹·¸ =«1.0 x» « = (k ) After KQ20( k.0) (0m3, n.0) ¸ =05«.1.0 ¨¦ ( k )»0 0( Steve 0 0 0 )= 0« ¦ 0 »06.0«0 2.0 3.0 5.0 6.0« m »10 n 10 © ¹ 1© n 1 mrofnoc « m » ycneiciforp ¹ « » « « » T )evetbe tS ( ) ES0(QK 0 S ( ecnareputation K x )0treb)0l== A0.6, (0., Christ’s LetChrist knowledgereputation reputation ’s knowledge Let ) ES00 ( K 00) tre0b0lA0,eve0 00 0then 0¬ ¼ ¬ ¼ ¬ ¼0 0 0be 0K ( SE0)) ES0( ¬(Chris reputation be K (reputation (Chris ) = 0.6, then Let T tie Christ’s knowledge serialized KLet , Chris Chris )) x K 08.0.6, 0.0ª1.0 x 0.06 =00.6 0 0( Steve 0ª ,be 0reputation 4.(0reputation 00 ) 9=then ª º 0 0 0 0 0 0ª º0 KSE º0) 0KQ0( k )reputation Christ ’s knowledge Chris reputation SE ( k ) ( Steve ) 4.0(ºChris ( SE ) Christ ’s knowledge reputation be K ((reputation Chris ))== 0.6, then Let ( SE ) » »0 0 0 2.0 0 0« « » « »0 0 0 serialized tie 0««0.6 1 0 ( Steve 0« ,»Chris 0 0 ) x1K .0( SEreputation 2.0 »(0Chris 3.00 )30.=0« 12.0 x 00.6 = K ( ktie) ( Steve, Chris ) KQ( kserialized ) ) « = » = x , Chris ) x Kreputation 0.6 Ktie ) KQserialized ( Steve (Chris)) == 11=..00xx00..66 == »0.6 ( k ) ( Steve, Chris K »0 )0 KQ 0« » 0 0 0 0 0 0« 0 ((k0k)) 0 ( Steve 0« », 0Chris 0 ) x2.K0 ((SESE3)).0 »0(Chris 5.00 60.0« 0 ( k ) ( Steve, Chris As a result, Steve tie to Chris»is 0.6. « «» « « » » ’s knowledge relationship 0 0 0 relationship 0¬ ¼0 0tie to 0 Chris 0 is 0 0 0 ¬ 0 0 0¬ ¼ 0 0 0 0 0 0¬ ¼00.. ¼0 0knowledge As a result, Steve’s As a result, Steve’s knowledge relationship tie to Chris is 0.6. As aa result, result, Steve Steve’s ’s knowledge knowledge relationship relationship tie0to to Chris Chris is 0.6. 0.6. 0 0is 0 0ª º0tie As »0 Chris In summary, this example illustrates that is more than Albert in terms 9of helping 0 2.0 0knowledgeable 0«« » 0 Engineering”. = Steve to apply conceptual knowledge of“Software » 0 0 0 0 0 0« 9 » « 9 9 ¼ 0 0 0 0 0 0¬ 8

8

8

4. Calculation of Social Relationship Tie

Social interaction ties are the structural links created through the social interactions between individuals in a network(Burt, ; Putnam, ; Wasko, and Faraj, 00; Zhang, G. Q., Jin, Q. and Lin, M., 00). Prior studies suggested that an individual’s centrality in an electronic network of practice can be measured using the number of social ties an individual has with others in the network (Ahuja, et al., 00). Some academics addressed the importance of social interaction ties in knowledge exchange. For example, Tsai and Ghoshal (Tsai, Ghoshal, ) found that social interaction tie has positive impacts on the extent of inter-unit resource exchange. Wasko and Faraj (Wasko, and Faraj, 00) found that the centrality built up by the social interaction ties that any individual creates in a network significantly and positively impacts the helpfulness and volume of knowledge contribution.

. Rationale and Equations The social relationship tie indicates the degree of social familiarity between pairs of peers on the S-net. For a pair of peers, as denoted by peer i and peer j, the social relationship tie between them is the product of their social familiarity, social reputation, and social trust.

detailed discussions about our calculations using the rating mechanism regarding trust and detailed discussions about our calculations using the rating mechanism regarding trust and Journal of Scientific and Technological Studies, (), -(00)  reputation. reputation. detailed discussions about our calculations using the rating mechanism regarding trust and Huhns and Buell (Huhns, and Buell, 00) pointed out that people are more likely to trust something detailed about our calculations the rating mechanism regarding trust and y reputation. S tie , j ) discussions S familiarit S reputation ( j )more u S trust (i, using j ) to collaborate tie (iproved. y (i , j ) u Similarly, are with someone with good reputation. 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To perform quantitative analysis, we between -1 1, indicating the relationship is good or bad. To perform quantitative analysis, we define social between peers isocial and j as follows: = 0.5~0.7, if the peer i considers peer j aorteam-mate with positive relationship S familiarit (familiarity i, jmembers. ) 1, community Meanwhile, familiarity can be positive or negative values ranging y and between -1 indicating relationship is good bad. To perform quantitative analysis, we 0.5~0.7, if peer i considers peer j a team-mate with positive relationship S ( i , ) community members. Meanwhile, social familiarity can be positive or negative values ranging define social familiarity between peers i and j as follows: 0.3~0.4, if peer i considers peer j an organization colleague with positive relationship = 0, if there is no relationship between peer i and peer j. j between -1 1, indicating the relationship isas good or bad. To perform quantitative analysis, familiarit y and between -1 and 1, indicating the relationship isasjgood or bad. Towith perform quantitative analysis, wewe define social familiarity between peers i and j follows: define social familiarity between peers i and follows: S ( i , j ) = 0.8~1.0, if peer i considers peer j a friend positive relationship y -1 and 1, indicating the relationship is good or bad. To perform quantitative analysis, we between define social between peers i and j as S familiarit 0.3~0.4, ifispeer i considers j an organization colleague with positive 0~0.2, if the peer i considers peer jpeer afollows: virtual community member with positive relationship 0, if there no relationship between peer and peer j. j) = familiarit y (i , familiarity between -1 and 1, indicating relationship or Toiwith perform quantitative analysis, we define social between peers i and as follows: 0.3~0.4, peer considers peer an organization colleague with positive define social familiarity between peers i and jisasjgood follows: S familiarit i, familiarity j) = 0.8~1.0, ifif peer i iconsiders peer j ajbad. friend positive relationship y( 0.5~0.7, if peer i considers peer team-mate with positive relationship S familiarit ( i , ) -0.8~-1.0, if peer i considers peer j a friend with negative relationship = 0, if there is no relationship between peer i and peer j. j define social familiarity between peers i and j as follows: y relationship define social between peers i and j aspeer follows: S familiarit (familiarity iy, j ) = 0.8~1.0, if peer i considers j a friend with positive relationship S familiarit y y (i , j ) = 0, if there is no relationship between peer i and peer j. relationship ififispeer i iconsiders team-mate with positive relationship S familiarit (i, j ) j ) 0.5~0.7, -0.5~-0.7, peer considers peer peer j a team-mate with negative 0, if there no relationship between peer i and peer j. j. relationship = 0, if there is no relationship between peer i and peer S familiarit y y (i , = familiarit 0.3~0.4, if peer i considers peer an organization colleague with positive S familiarit ( i , j ) = 0.8~1.0, if peer i considers peer j aj friend with positive = 0, if there is no relationship between peer i and peer j.relationship S familiarit ( i , j ) familiarit y yy 0, if there ispeer no relationship between peer iwith and peer ififispeer i iconsiders peer aj team-mate with positive relationship S familiarit (i, ) j 0.5~0.7, -0.3~-0.4, considers peer j an colleague with negative relationship if there no relationship between peer i and peer j. j. relationship S familiarit = 0.8~1.0, ifpeer i considers peer aorganization friend positive y y (jiy, =) 0, familiarit 0.3~0.4, if peer peer an organization colleague with positive S familiarit (i, j ) = 0.8~1.0, considers peer jbetween ajj friend with positive relationship S familiarit 0.8~1.0, ifpeer peer iconsiders considers peer a friend with positive 0, if if there isii ino relationship peer iwith and peer j.relationship S relationship y y (i ,(ji ,) j= considers peer aj team-mate positive relationship S familiarit S familiarit j ) 0.5~0.7, =) = 0.8~1.0, ifno peer i considers peer apeer friend with positive 0~-0.2, ififpeer i considers peer j a jvirtual community member with negative relationship 0, if there ispeer relationship between i and peer j. relationship y (iyy, (ji), = S familiarit ( i , j ) = 0.8~1.0, if peer i considers peer j a friend with positive relationship S familiarit ( i , j ) = 0.3~0.4, if peer i considers peer j an organization colleague with positive 0.8~1.0, if peer i considers peer j a friend with positive relationship 0.5~0.7, team-mate with positive relationship y y y relationship familiarit = 0.5~0.7, if peer i considers peer j a team-mate with positive relationship S familiarit ( i , j ) = 0.5~0.7, if peer i considers peer j a team-mate with positive relationship S familiarit ( i , j ) S y y (i,(ji,) j= )= 0.8~1.0, if peer i considers peer aorganization friend with positive relationship 0.3~0.4, peer an colleague with positive 0.5~0.7, ifpeer peer iconsiders considers peer aj team-mate with positive relationship S familiarit S familiarit ifif peer i iconsiders peer j ajj friend with positive relationship familiarit 4.1.2. Social reputation y (iyy, j ) = 0.8~1.0, relationship 0.5~0.7, if peer i considers peer j a team-mate with positive relationship i , j ) = 0.5~0.7, if peer i considers peer j a team-mate with positive relationship S familiarit ( i , j ) S familiarit ( = 0.3~0.4, if peer i considers peer j an organization colleague with positive y y y familiarit familiarit S familiarit (i, j ) = 0.3~0.4, if peer i considers peer j an organization colleague with positive S familiarit 0.3~0.4, if ifpeer i iconsiders peer jj an organization colleague with positive 11 = 0.5~0.7, peer considers peer a team-mate with positive relationship S ) y y (i ,(ji ,) j= relationship S familiarit (ji), = j )has = 0.3~0.4, if peer i considers peer j an organization colleague with positive 0.5~0.7, if peer i considers peer j aproduct team-mate with positive relationship S familiarit 11 Each a social reputation, which is the of the peer’s social rating (Putnam, y (iy,peer relationship S ( i , j ) = 0.3~0.4, if peer i considers peer j an organization colleague with positive S (i, jy) = 0.3~0.4, if peer i considers peer j an organization colleague with positive ) and familiarit relationship relationship S y relationship (i, j ) peer’s = 0.3~0.4, if peer i considers peer j an organization with positive the average social represents a colleague degreewith of confidence S familiarit (irelationship , jof ) =the0.3~0.4, if peerfamiliarities. i considers Social peer jreputation an organization colleague positive to a target relationship 11 peer from all the other peers on a social network who know the target peer. The social reputation of peer j relationship relationship 11 is computed as follows: 11 11

11 11 11 11 11 11 11

11

4.1.2. Social reputation Each peer has a social reputation, which is the product of the peer’s social rating (Putnam, Each peer has a social reputation, which is the product of the peer’s social rating (Putnam, 1995) and the average of the peer’s social familiarities. Social reputation represents a degree of 1995) and the average of the peer’s social familiarities. Social reputation represents a degree of confidence to a target peer from all the other peers on a social network who know the target confidence to a target peer from all the other peers on a social network who know the target peer. The social reputation of peerofj Communities is computedofasPractice follows: The Identification in Web .0 through Social Networks peer. The social reputation of peer j is computed as follows: S

>



@

S reputation ( j ) = AVG S familiarity ( j ) u S rating ( j ) ( j ) = AVG S familiarity ( j ) u S rating ( j )

>

reputation

=

m 1

¦ >S

NoP ( m )

¦= >S

NoP ( m )

@

familiarity

@

( j , m) ( j , m) u S rating ( j ) , NoP( j ) u S rating ( j ) , NoP( j ) familiarit y m 1

@

where: where: where: AVG S familiarity ( j ) is an average value of peer j’s social familiarities, averagevalue valueofofpeer peerj’sj’ssocial socialfamiliarities, familiarities, AVG S familiarity ( j ) is an average S rating ( j ) is peer j’s social rating, is peer peer j’s j’ssocial socialrating, rating, S rating ( j ) is NoP(j) is the number of peers connected to peer j. NoP(j) is is the NoP(j) the number number of ofpeers peersconnected connectedtotopeer peerj.j.

>

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@

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4.1.3. Social trust 4.1.3.trust Social trust 4.1.3. Social Social trust is a confidence of how a pair of peers on the social network treats each other. It Social trust of how pairbetween ofpeers peers on the social network each other. requested peeraispeer j,a confidence the trust a association the two peerstreats is todenoted by S trust (i, j )It. also Social trust is a how confidence of how pair of apeers on the social network each other. Itsocial also indicates is associated with other directly connected hertreats on the trust trust indicates how a peer is associated with other peers directly connected to her on the social network. also requested indicates how associated with other peers the directly her peer on by the social network. pair ofispeers are socially related, as denoted the requesting peer ithe and j,peer the trust association twoofconnected peers is to denoted (i, requested jthe ) . trust For S For (peer i, ja)aindicates the who confidence ofbetween trustworthiness thebyrequesting i Sto trust trust requested peer j, the trust association between the two peers is denoted by S ( i , j ) . requested peer j, athe trust association between the two peers isby denoted by Sj, i the (peer iis , jtrust . j, association requested requested peer the trust between between two pe th requested peer j,arethe trust related, association between therequesting two peers by peer S association j ) . the network. pair of peers whosocially socially as denoted the peer i)denoted and j,(i,the a For peers who are related, as denoted by requesting peer S trust (pair i, j ) of indicates of trustworthiness of the the requesting peer iand to the thetorequested requested used to determine whether the requested peer conforms the requesting peer j. trust S trust (i,the j ) isconfidence trust trust trust requested S trust ( i , j ) indicates the confidence of trustworthiness of the requesting peer i to the S (i, j ) indicates theSconfidence of trustworthiness thetrustworthiness requesting to requested j,confidence the trust association between therequ tw (irequested , of j )i.indicates S the (i,peer j )the indicates of trustworthiness of trustworthiness of the (i, j )between indicates thetwo confidence of the requesting peerthe i toconfidence the requested trust association the peers of is denoted by S peer indicates the confidence to whether the requested conforms topercentage, the requesting S trust (requirements i, j ) is used peer’s of determine trustworthy. The value of S trust (i, peer denoted bytrust the higher j ) istrust trust peer j. trustthe requesting trust to determine whether the requested peer conforms to peer (i,(ij,trustworthiness ) jis) isused requested peer j, the trust used to determine whether the requested peer conforms to the requesting peerj. j.S S trust S ( i , j ) indicates the confidence of trustworthiness of th i j. peer to the requested peer isisSused determine whether the the used used to determine whether whether requested the pe re peer (i, jexample, j. )peer (ito, to jif ) isdetermine to determine whether the j.requested conforms to value the requesting peer j. of S theis, (irequesting , jthe ) is used trust confidence higher the trust is.S For the of peer’sthe requirements of trustworthy. Thetrust value of S association by percentage, the higher (i, j ) is denoted trust trust trust trust trust trust 12 peer’s of trustworthy. The of by thethe (i,(ij,peer’s ) jisThe S the (i,higher j )whether indicates confi requested peer, Albert conforms to the requesting requirements trustworthy. value is peer’srequirements requirements trustworthy. Thevalue value ofS S means isdenoted denoted bypercentage, percentage, higher )the is used toof determine peer (higher i78% ,of j ) The peer’s requirements peer’s requirements of trustworthy. The value The of S value is (the ithe , jof )reques Sden requirements of which trustworthy. value of by percentage, (ofi,Chris j )j.isSdenoted S trustof (peer’s Chris 78%, requesting peer has confidence that the the confidence is, the) is higher the trust association is. SFor example, if trustworthy. the value 12 of trust trust thetheconfidence is, the higher the trust association is. For example, if the value of confidence is, the higher the trust association is. For example, if the value of denoted by percentage, the higher the confidence is, the higher the trust association is. For example, if the trust is used peer j. S ( i , j ) the confidence the confidence is, the higher is, the the higher trust association trust associati is. the confidence is, the higher the trust association is. For example, if the value of peer’s requirements of trustworthy. The value of S (ito ,Fo j) peer Albert trustworthy. S (requested Chris, Albert ) is 78%,iswhich means the requesting peertrustChris hastrust 78% confidence that the trust which means the requesting peer Chris has 78% S trust (Chris , Albert ) is 78%, confidence that the S trust (Chris , Albert ) is 78%, which means the requesting peer Chris has 78% confidence that the S (Chris Sconfidence , Albert (Chris ) is , 78%, Albert )confidence which ishigher 78%, means which the requesting means the requestin peer Chr S Albert (Chrisis, Albert ) is is %, 78%, which which means meansthe the requesting requesting peer Chris has 78% confidence that the association value ofpeer Chris has % that the the peer is, the the trust peer’s requirements of trustw requested trustworthy. trust requested peer Albert trustworthy. requested peer Albert is trustworthy. requested peer requested peer ison trustworthy. requested peeris Albert is trustworthy. (Chris Albert 78%, which means the requesting pe requested peer Albert trustworthy. We isutilize sampling of binomial probability to calculate theSvalue ofAlbert S, trust (isi, trustworthy. j))Albert ,isbased a 95% the confidence is, the hig trust trust peer trust association requested peer is confidence interval in termsprobability of probability (Mitchell, We the following terms Sj, the (Chris , Albert ) is 78%,be We utilize sampling of binomial to calculate the1997). value of Sfirst (define i, j )requested ,Albert based ontrustworthy. a 95% trust trust We probability to SWe (utilize i,(ij,) j, )based ononaofof 95% Weutilize utilizesampling samplingofofbinomial binomial probability tocalculate calculatethe thevalue valueofof S trust ,We based asampling 95% trust sampling utilize binomial of probability binomial probability to calculate to calculat value We utilize sampling of binomial probability to calculate the value S ( i , j ) , based on a 95% requested peer Albert istrustw trustw S (i, j ) indicates confidence ofthe confidence in terms ofofprobability (Mitchell, 1997). We first We interval utilize sampling binomial probability to calculate thedefine valuethe of following ,terms based onthea % confidence interval inin terms (Mitchell, 1997). We first define the following terms confidence interval terms probability 1997). We first define the following terms „confidence Sofof isprobability a set of interaction instances representing samples offirst the requested peer’s past confidence confidence interval in interval terms of in probability terms of probability (Mitchell, 1997). (Mitchell, We 19 fi interval in(Mitchell, terms of probability (Mitchell, 1997). We define the following terms We utilize sampling of binomial probability to calculate th trust confidence interval in terms of probability (Mitchell, ). We first define thepeer following j. S terms (i, j ) is used to determine wheth ^ ` S s , s ,.... s . interactions, confidence interval inpeer’s termspast ofWe probability (Mitchell, „ SS is a set of interaction 1 instances of the requested 2 n representing samples utilize sampling of1997) binom is a „ set of instances interaction instances representing samples of Sthe peer’s pastinteraction interactions, „„ S Sis isa asetset2 ofofinteraction representing samples ofrepresenting thetherequested peer’s of trustworthy. The valu instances representing samples of „requested is requested aexperience „peer’s set peer’s Sofpast ispast interaction a requirements set of instances representing instances represent sample a^sset of interaction instances samples the requested peer’s past „ interaction Tr is SaSisset of trust evaluation values containing past instance, and is ` , s ,.... s . interactions, confidence interval in terms o 1 2 n ` , s ,.... s . interactions, the confidence is, the higher the trust ^ ` s , s ,.... s . interactions,S S ^sdenoted 1 2 interactions, n n Sset ^of s1 ,interaction s 2 ,.... S s ^`s. , s 2 ,....s n `. representing interactions, ^s1tr, sn2values „ S is interactions, ainstance, `,.... . s n `.containing past experience Tr ^evaluation tr1 S, tr2 ,.... „ Tr is a 1set2 ofbytrust and isn 1 instances trust Sand (is Chris ,evaluation Albert ) is 78%, means the „„ TrTris isa aset2 of evaluation values containing past experience instance, and is Troftrust is trust a„set of trust evaluation values containing past experience instance, and is sdenoted setdenoted evaluation values containing past experience instance, ^ `.by S interactions, „ Tr is „ a set Tr of is trust a set of containing values con pas Tr is a set of trust evaluation values containing past experience instance, and „sevaluation S isiswhich a set of intera nvalues ^.tr1Tr by Tr: S o , tr2Rating ,....trn `.s : The Rating function maps the interaction instances1s,trust „ Rating to2 ,.... past ` denoted byby TrTr ^tr^1tr , tr ,.... tr requested peer Albert is trustworthy. . ` denoted , tr ,.... tr 2 2 denoted n n ^trservice ^ntr`evaluation denoted Trapast tr2Tr ,....trinstance . by Tr ^tr. 1 In , tr2other ,....trn `words, S ^s interactions, „associates Trbyisdenoted set of 1 ,by 1., tr2 ,....tr n ` .values containi function experience instance, tr s : function „ Rating :1 S o Tr Rating The Rating functionthe maps the interaction instance s totrust past  „„ Rating : S: So Tr s : The Rating maps the interaction instance s to past 2 Rating: S→Tr Rating(s): The Rating function maps the interaction instance to past experience  Rating o TrRating Rating s : The Rating function maps the interaction instance s to past ^ ` denoted by . Tr tr , tr ,.... tr „are Rating :the S by o Rating Tr Rating : Sother oinstance Tr s1 than : Rating The : The function funct s :the „ past Rating : S tro. Tr The Rating function maps„past interaction sRating to „ Tr is Rating a set maps of trut 2 ns past with experience instance, experiences collected peers the In Rating other words, the function associates service instance experience instance, We utilize sampling of binomial probability t .trexperience In other words, the function associates past service instance experience tr tr . In other the function associates past service instance experienceinstance, instance, instance, . words, the function associates past service instance with past experience ^ denoted by requesting peer. Tr tr1am . In other words, . In other the words, function the experience experience instance, instance, . In other words, the function associates past service instance instance, tr tr  „ Rating : S o Tr Rating s : The Rating function tr with past experience instance, the experiences are collected by peers other than the confidence interval in terms of probability (M with instance, areare byby peers other than the withpast pastexperience experience instance, experiences collected peers other than the with past experience with past experience instance, the instance, experiences the are col with instance, the other experiences are collected by peers other than the ^the `are „ Accpet : Trpast othe 0experience ,1experiences A requirement hypothesis can be denoted as Accpet function. instance, the experiences collected bycollected peers than the requesting peer. In other words, the experience instance, requesting peer. tr .The „ Rating : S experienc o Trfunc Ra requesting peer. requesting peer. requesting requesting peer. peer. requesting peer. output function ishypothesis 1 when past experience instance is accepted by the experience with past experience instance, the experiences a ccpet: Accpet „ 2A Accpet :Tr Tr → oof^0Accept ,1` A requirement can denoted Accpet function. The hypothesis can be be denotedasas function. The output of instance „ S is a^0function. set of requirement interaction instances ^0^,10requesting `,„ „„ Accpet : Tr oo hypothesis can asasAccpet function. The Accpet : Tr 1`A Arequirement requirement hypothesis canbebedenoted denoted Accpet function. The ^ ` ` „ Accpet „ : Tr Accpet o 0 , 1 : Tr A o requirement , 1 A hypothesis hypothesi can be de ^ ` Accpet : Tr o 0 , 1 A requirement hypothesis can be denoted as Accpet The peer, otherwise is 0. requesting peer. output Accept isexperience 1 when past experience instance acceptedpeer, by otherwise the Accept of function is 1function when past instance is accepted by the is requesting 0.^s , spast Sis with ,....sexperien interactions, n `. output is past experience is isexperience accepted outputofofAccept Acceptfunction function isof1when when experience instance accepted bythe output ofbyoutput Accept function function 1 by when 12 when experienc output Accept function is 1 instance when past instance is^Accept the1ispast „ Accpet :the Tr of o 0accepted ,1` A isrequirement hypothesis ca requesting peer.past requesting peer, otherwise is 0.past requesting peer, otherwise is 0. „ Tr is a set of trust evaluation va requesting peer, otherwise is 0. peer, otherwise is 0. ­1 Accept requesting requesting peer, otherwise peer, is otherwise 0. is 0. requesting output of Accept function 1 when „ is Accpet : Trpast o ^0exp ,1` Accpet tr { ® denoted by Tris 0.^tr1 , tr2 ,....trn ` . 0 otherwise requesting peer, otherwise ­1 ¯Accept output of Accept 1­1 Accept Accept tr { ® ­Accpet 1 Accept ­1 sAccept ­1 Rat ­ : {The „ Rating : S o Tr Rating  Accpet tr { 0 otherwise Accpet tr ®{ ®  Accpet tr requesting {Accpet tr peer, ¯  Accpet tr { ® ® ® oth 0 otherwise ¯ ¯0 otherwise ow 0 a Binomial otherwise Based ofofHypothesis for Proportion to to evaluate thethe simple ¯0 tr otherwise . In experience instance, ­1 other Basedononthe theusage usageofofLarge-Sample Large-Sample Hypothesis a Binomial Proportion evaluate ¯0Acce ¯for Accpet tr { ® with past experience instance, the simple error and true error of a hypothesis addressed in (Mitchell, 1997; Mendenhall, and error hypothesis addressed in (Mitchell, Mendenhall, andtoBeaver, ), Basedand ontrue the error usageofofa Large-Sample of Hypothesis for a; Binomial Proportion evaluate the the result ¯0 otherw Based thetheusage ofofLarge-Sample Hypothesis forfora Binomial Proportion evaluate the Basedonon usage Large-Sample aassesses Binomial Proportion evaluate the requesting peer. 1999), the result of the the sample istoaato Boolean value (true or false). Based the Based usage oncan of Large-Sample usage of Hypothesis for a Bino Based onerror the of usage of hypothesis Large-Sample of Hypothesis foron Binomial Proportion toLarge-Sample evaluate the of Hypothesis simple error and true ofof aHypothesis hypothesis addressed in (Mitchell, 1997; Mendenhall, and of Beaver, the hypothesis assesses the sample is a Boolean value (true or false). Thus we see that of the hypothesis simple and true error ofof a ahypothesis addressed 1997; Mendenhall, and simpleerror error andThus true error hypothesis addressed in(Mitchell, (Mitchell, 1997; Mendenhall, and we can see that the hypothesis the sample as aerror Bernoulli trial and distribution „ the Accpet : Tr 0and ,of 1` Hypothesis A requirement simple simple andon true error error true of aLarge-Sample error hypothesis of o a ^hypothesis addressed addresse in (Mitc simple error and true error ofassesses ainhypothesis addressed in (Mitchell, 1997; Mendenhall, Based the usage of for Beaver, 1999), the result of the hypothesis assesses the sample is a Boolean value (true or false). assesses the sample as a Bernoulli trial and the distribution of Bernoulli trial is a binomial distribution. The Beaver, the result ofof the trial hypothesis assesses the sample is isa assesses Boolean value (true false). Beaver,1999), 1999), result hypothesis assesses the sample abinomial Boolean value (true false). of Bernoulli is hypothesis athe binomial distribution. The distribution approximates the(true 1999), Beaver, the 1999), result ofthe the result hypothesis ofnormal hypothesis assesses the assesses sample the isin Beaver, 1999), result of the hypothesis the sample isorand aor Boolean value or false). output ofthe Accept function is 1 w simple error and error of a Based hypothesis addressed Thusthe we can see the that the assesses the sample asBeaver, a Bernoulli trial thetrue distribution on the usage of La distribution approximates the normal when the number ofthe sample issamples enough. Simple Thus seebinomial that hypothesis assesses thethe sample asas aassesses Bernoulli trial and the distribution Thuswewecan can thatthethe hypothesis assesses sample adistribution Bernoulli trial and the distribution distribution when the number of sample is enough. Simple error is correct inand and Thus we can Thus we thatcan see hypothesis that assesses the assesses samplethe as asamp Ber Thus we see thatdistribution. the hypothesis the sample as asee Bernoulli trial thehypothesis distribution requesting peer, otherwise is 0. Beaver, 1999), therate result ofthe the hypothesis assesses the sam of see Bernoulli trial is acan binomial The binomial distribution approximates the normal simple error and true error ofofBernoulli trial distribution. The distribution approximates thethe normal Bernoulli trialis isa abinomial binomial Thebinomial binomial distribution approximates normal true error is correct rateisin We will Simple getThe a of confidence interval the simple Bernoulli ofwe trial Bernoulli israte aaccording binomial is to adistribution. binomial distribution. The binomial The distrib binom of Bernoulli trial binomial binomial distribution approximates the normal Thus can see that the hypothesis assesses the result sample distribution when thedistribution. number ofapopulation. sample is distribution. enough. error is correct intrial samples and Beaver, 1999), the ofas th distribution thethenumber sample isthe Simple error isenough. rate in distributionwhen when number of sample isenough. enough. Simple error iscorrect correct rate insamples samples and error and theofrate area confidence interval represents a probability which true fall in the distribution distribution when the number when the of number sample of is enough. sample isSimple enough. error Si when number of sample Simple error is and correct rate inThus samples and of Bernoulli trial istoaerror binomial distribution. The binomial true error isdistribution correct inofpopulation. We will get a is confidence interval according the simple we can see that the hyp tri true rate true in population. We will get a confidence interval according totoerror thethe simple trueerror erroris iscorrect correct population. We will athe confidence interval according simple interval. Inerror theofnormal distribution, true error isprobability 95% probabilities falling within the range ofsimple true istrue correct rate is correct in population. rate population. We will get We a confidence willAccpet get a co isconfidence correct rate inget population. We get aerror confidence interval according to the distribution when the number of sample istrial enough. Simpl error andrate the inarea interval represents awill which true error fall in in the of Bernoulli is a binomi error and the area of confidence interval represents a probability which true error fall in the error and the area of error confidence interval probability which true error fall inconfidence the mean r 1.96 u SD (Standard Deviation) in is compliance with experience rule. Inrange words, error and the error area and ofthe area of confidence interval interval represents a probabili and the area ofrepresents confidence interval represents athe probability which true error fall represents inWe thewhen true error is correct rate inother population. will get confida interval. In the normal distribution, the true aerror 95% probabilities falling within the of distribution theanumbe interval. normal distribution, the true error is is95% probabilities falling within the range ofof interval.InInthe the normal distribution, the true error 95% probabilities falling within the range we can utilize the confidence interval to evaluate lowest true error of the evaluating hypotheses. interval. In interval. the normal In the distribution, normal distribution, the true error the is true 95% error probabil is 95 interval. In the normal distribution, the true error is 95% probabilities falling within the range of and the area of confidence interval represents mean r 1.96 u SD (Standard Deviation) in compliance with the error experience rule. InBased other words, error correct rate ain po on thetrue usage of is Large-Sample ofpro H mean 1.96 uu SDSD(Standard Deviation) in compliance with thethe experience rule. In other words, meanr r 1.96 (Standard Deviation) in compliance with experience rule. In other words, Let function be the hypothesis and then we can evaluate the possible true error of the Accpet mean r 1.96 mean u SD r 1.96 (Standard u SD Deviation) (Standard Deviation) in compliance in compliance with thepre mean r 1.96 u SD (Standard Deviation) in compliance with the experience rule. In other words, In thesimple normal distribution, the true error is 95% we can utilize the confidence interval to evaluate lowest true errorinterval. of the evaluating hypotheses. error and the area of confid error and true error of a hypothesis

10

Journal of Scientific and Technological Studies, (), -(00)

error is correct rate in samples and true error is correct rate in population. We will get a confidence interval according to the simple error and the area of confidence interval represents a probability which true error fall in the interval. In the normal distribution, the true error is % probabilities falling within the range of (Standard Deviation) in compliance with the experience rule. In other words, we can utilize the confidence interval to evaluate lowest true error of the evaluating hypotheses. Let function be the hypothesis and then we can evaluate the possible true error of the hypothesis based on the past instances according to the Evaluating Hypotheses theory (Mitchell, ). Whether the tr (tr jE ) is accepted by is a binomial distribution which approximates the normal distribution when the number of samples is large enough. Thus we can utilize the normal distribution to calculate that the sample utilize the the normal normal distribution distribution to to calculate calculate that that the the sample sample error error closes closes with with the the true true error. error. The The utilize the normal distribution calculate the sample error closes with the true error. The utilize error closes with the95% true error. Thetotrue errorwithin isthat of % probabilities falling within a confidence interval, true error is of probabilities falling a confidence interval, which will be approved as true error error isis of of 95% 95% probabilities probabilities falling falling within within aa confidence confidence interval, interval, which which will will be be approved approved as as true which will be approved as a trustworthy peer in the general application. a trustworthy peer in the general application. trustworthy peer peer in in the the general general application. application. aa trustworthy We define the confidence symbol as the the lowest lowest bound oftrue theerror. true error. error. The of trust of service service We define thethe confidence symbol as the lowest bound of theof The trust service conforms We define the confidence symbol as the lowest bound of the true error. The trust of service We define confidence symbol as bound the true The trust of conforms to the request’s requirement when the confidence is higher. conforms torequirement the request’s request’swhen requirement when the the confidence isis higher. higher. to the request’s the confidence is higher. conforms to the requirement when confidence

u111  pˆppˆˆ 1 pˆppˆˆ uu SD =11 ¦Accpet AccpetRating Rating(((sss))) ,,, SD 96 95% SD Accpet Rating 96 ,,, zzz9595 pˆppˆˆ== 111...96 ¦ % ¦ % ୰˦ nnn sss୰୰ nnn ˦˦ Confidence {{ { max max^^^pˆppˆˆ   zzz 95% uu u SD SD,,,000``` Confidence max Confidence 95% % SD 95 As the the number number of of samples samples increases, increases, the the standard standard deviation deviation decreases decreases relatively relatively and and the the As the of samples increases, the standard deviation decreases relatively and the As As the number number of closer samples increases, the standard deviation decreases relatively and the confidence confidence will be to the true error. For example, the past instances of a requested peer is confidence will will be be closer closer to to the the true true error. error. For For example, example, the the past past instances instances of of aa requested requested peer peer isis confidence willdenoted be closer to the true error. For example, the past instances of a requested peer is denoted as S, and denoted as as S, S, and and let let SS The requesting requesting leaner leaner proposes proposes aaa Requirement Requirement Hypothesis Hypothesis Accpet Accpet... let S 256 256... The as S, and let The requesting leaner proposes Requirement Hypothesis Accpet 256 denoted . The proposes aisRequirement the be result of calculation , the confidence theHypothesis confidence. If can calculated from is the following If requesting the result result leaner of calculation calculation the confidence can be calculated calculated from the following the result of calculation confidence can be from the following IfIf the of isis pˆppˆˆ 000...666 ,,, the can equation. be calculated from the following equation. equation. equation. pˆppˆˆ

Confidence Confidence Confidence

111 ¦ Accpet Rating(s) 0.6 , z 95% 1.96 96 AccpetRating(s) Rating(s) 00..66,, zz9595%% 11..96 Accpet ¦ 256¦ sS 256 256 S ssS pˆ u 1  pˆ  zzz 95% uu u pˆpˆ uu11 pˆpˆ ## # 000...666  000...060012 060012 000...539987 539987 060012 539987 pˆppˆˆ  95 % 95 % 256 256 256

trust trust (i , j ) is.%, 53.99%, which means the requesting peer has The calculated confidence, i.e. SSStrust 53.99%, which means the requesting peer has The calculated confidence, i.e. 53.99%, which means requesting has The ((ii,, jj)) isisis Thecalculated calculatedconfidence, confidence, i.e. i.e. which means the the requesting peerpeer has .% 53.99% confidence that the requested peer can meet the trustworthy requirement based on 95% 53.99% confidence confidence that the the peer requested peer can can trustworthy meet the the trustworthy trustworthy requirement based on 95% 53.99% that requested peer meet requirement 95% confidence that the requested can meet the requirement based onbased % on confidence confidence interval. interval. Hence Hence we we can can assert assert that that the the trustworthiness trustworthiness of of the the requested requested peer peer isis is 56.83% 56.83% confidence interval. Hence we can assert that the trustworthiness of the requested peer 56.83% confidence interval. Hence we95%) can assert that thetotrustworthiness of the requested peer is .% (.% over %) (53.99% over conforming the requesting requesting peer’s peer’s requirements. (53.99% over 95%) conforming conforming to the the requesting peer’s requirements. (53.99% over 95%) to requirements. conforming to the requesting peer’s requirements.

4.2 Examples Examples and and Discussions Discussions 4.2 Examples and Discussions 4.2 .

Examples and Discussions

Based on the aforementioned equations and the examples examples shown in,Figure Figure 1, the the detail of Based on on the the aforementioned equations and the examples shown in Figurein the detail calculation Based on the aforementioned equations and the examples shown in Figure 1, the detail Based aforementioned equations and the shown 1, detail social relationship tie is illustrated as tie follows: calculation of social social relationship tie isis is illustrated illustrated as as follows: follows: calculation of social relationship tie illustrated as follows: calculation of relationship

Let: () Albert be a good friend of Steve’s, . familiarity familiarityy ( Steve, Albert ) Let: (1) (1) Albert Albert be be aaa good good friend friend of of Steve’s, Steve’s, SSS familiarit Let: (1) Albert good Steve,,Albert Albert)) 000...999... Let: ((Steve () Albert’s socialberating be friend 0.. of Steve’s, (2) Albert’s Albert’s social rating beconnected 0.6. () Albert has threesocial peersrating directly to him (Steve, Chris, and Bob). (2) Albert’s social rating be 0.6. (2) be 0.6. () Steve’s degreehas of trust Albert is 0.connected to him (Steve, Chris, and Bob). (3) Albert Albert threetopeers peers directly (3) Albert has three three peers directly connected to to him him (Steve, (Steve, Chris, Chris, and and Bob). Bob). (3) has directly connected (4) Steve’s Steve’s degree degree of of trust trust to to Albert Albert isis is 0.57 0.57 (4) Steve’s degree of trust to Albert 0.57 (4)

Then Albert’s social reputation is: Then Albert’s social reputation is: Then Albert’s social reputation is: of Communities of Practice in Web .0 through Social Networks The NoP Identification Then Albert’s social reputation is: ( 3) familiarit y ( 3) Then Albert’s socialNoP reputation S familiaritis: ¦ y ( Albert,3) S ( Albert,3) reputation ¦ m 1 u S rating ( Albert ) S ( Albert ) = NoP ( 3) reputation Then Albert’s social is: reputation m 1 u S rating ( Albert ) S ( Albert ) = NoPfamiliarit ( 3) NoP y ( Albert ) S ( Albert , 3 ) familiarit y ( Albert ) ,3) ¦ NoP¦(3)SNoP ( Albert familiarity familiarity ) = m 1 familiarit y y S ( Albert u,3S)rating ( Albert S reputation ( Albert reputation m 1 )u S ¦ S familiarit , Steve , Chris )(uAlbert S) familiarit , Bob) y ( Albert familiarit y ( Albertu familiarit S rating ) y (( Albert ( Albert ) = NoP ( Albert ) rating) u S S rating ( Albert ) =S reputation m 1 S ( Albert , Steve ) S ( Albert , Chris u uS ( Albert ) Albert, Bob) u ( Albert ) = NoP( Albert ) 3 u S rating ( Albert ) =S NoP( Albert )3 y y S familiaritfamiliarit ( Albert, Steve) u S familiaritfamiliarit ( Albert, Chris) u S familiarity ( Albert , Bob) y 0.9  0.8yy ( Albert (0.2), Steve) u S familiarityy ( Albert, Chris) u S familiarit S familiarit ( Albert, Bob u S)rating (rating Albert ) = 0.6 = = 0.9 (0.2), uSteve S  0.8 ( Albert ) u0.3 S ( Albert, Chris) u S familiarity ( Albert, Bob) u S rating ( Albert ) 3 u 0.6 = 0.3 3 uS ( Albert ) = 3 3 3 0.9  0.8  (0.2) 0.9  0.8  (u0.02.)6 = 0.3 = = 0.9 3 0.8  (0.2) u 0.6 = 0.3 = u 0.6 = 0.3tie to Albert is So Steve’s3 social relationship So Steve’s3social relationship tie to Albert is y S tie ( Steve, Albert ) S familiarit ( Steve, Albert ) u S reputation ( Albert ) u S trust ( Steve, Albert ) S tie (’sSteve , Albert ) S familiarit , Albert So Steve social relationship tie yto( Steve Albert is ) u S reputation ( Albert ) u S trust ( Steve, Albert ) SotieSteve’s Steve’s social socialrelationship relationshiptietietotoAlbert Albertisis So S tieSteve Albert )relationship ( Steve 0.9yu 0.3 utie 0.57 0.153is ’s, social to Albert tie So familiarit S ( Steve , Albert ) S) 0.9familiarit S reputation ( Albert ) u S trust ( Steve , Albert ) SS tie (( Steve ,, Albert u( Steve 0.3y u( Steve 0, Albert .57 , Albert 0).u153 Steve Albert ) S ) u S reputation ( Albert ) u S trust ( Steve, Albert ) tie familiarit y reputation trust S ( Steve , Albert ) S ( Steve , Albert ) u S ( Albert ) u S ( Steve , Albert ) S tie ( Steve 0.9 u 0.3 u 0.57 0.153 tie , Albert ) S tie ( Steve, Albert ) 0.9 u 0.3 u 0.57 0.153 familiarity Let (1), Chris ( Steve, Chris ) 0.8 S ( Steve Albertbe ) a0good .9 u 0.friend 3 u 0.57of Steve 0.153’s, S Let (1) Chris be a good friend of Steve’s, S familiarity ( Steve, Chris ) 0.8 (2) Christ’s social rating be 0.8 y ()Chris Chris good friend ofSteve Steve’s, Let Let:(1) bebeaa’s good friend of ( Steve 0.8 Christ social rating be 0.8’s, S familiaritfamiliarit (2) y , Chris ) Let (1) Chris be a good friend of Steve ’s, ( Steve , Chris ) ,0Albert .8 S familiarity (3) Chris also has three peers directly connected to him ( Steve , and Bob) Let () (3) (1) Chrissocial be good friend ’s, Sconnected ( Steve , Chris ) ,0Albert .8 Christ’s rating be0.8 0.of Steve Chris alsoa rating has three peers directly to him (Steve , and Bob) ’s social be (2) Christ Christ ’shas social rating be 0.8 (2) Steve ’s degree of trust to Chris is 0.57 () (4) Chris also three peers directly connected to him (Steve, Albert, and Bob) social rating (2) Christ Steve degree trustbe to0.8 Chrisconnected is 0.57 to him (Steve, Albert, and Bob) (4) (3) Chris also’s’s has threeof peers directly Chris also has threetopeers () (3) Steve’s degree of trust Chrisdirectly is 0. connected to him (Steve, Albert, and Bob) (3) Chris also of hastrust three ’s degree to peers Chrisdirectly is 0.57 connected to him (Steve, Albert, and Bob) (4) Steve ’s degree of trust (4) Steve Then Chris ’s social reputation is: to Chris is 0.57 ’s degree of trust (4) Steve Then Chris’s Chris ’s social social reputation Then reputation is:is: to Chris is 0.57

>>

>> > > >

@@ @ @ @

> > >

11

@@

@ @ @

>>

NoP ( 3)

@@

NoP ( 3) S familiarit Then Chris’s social reputation is: y (Chris,3) Then Chris’s social ¦ reputation is: (Chris,3) reputation ¦ m 1 S u S rating (Chris) (Chris ) S reputation = Then Chris ’s social reputation is: NoP ( 3) m 1 u S rating (Chris) (Chris ) = NoPfamiliarit S ( 3) NoP y (Chris ) S ( Chris , 3 ) familiarit y (Chris ) ,3) ¦ NoP¦(3)SNoP (Chris familiarit y familiarit)y = m 1 familiarit y u,3 y S ( Chris S)rating (Chris ( S reputation Chris reputation m 1 )u S ¦ S familiarit , Steve , Albert u) S familiarit , Bob) rating y (Chris familiarit y (Chrisu familiarit S rating ()Chris ) y ((Chris ( ) Chris = NoP ( Chris ) rating ) u S =SS reputation m 1 )u S S ( Chris , Steve ( Chris , Albert Chris, Bob) u S rating (Chris) (Chris) (Chris ) = NoP(Chris) 3 u S uS (Chris) = NoP (Chris) 3 familiarity familiarity familiarity S (Chris, Steve) u S (Chris , Albert ) u S (Chris , Bob) y y 0.8  0.8yy (Chris 0.4 , Steve) u S familiarit S familiarit (Chris, Albert ) u S familiarit (Chris, Bob u S)rating (rating Chris) = y .8 = 0.52 = 0.8  0.8 (Chris 0.4 u,0Steve S familiarit ) u S familiarit (Chris, Albert ) u S familiarity (Chris, Bob) u S rating (Chris) 3 u 0.8 = 0.52 3 uS (Chris) = 3 3 3 0.8  0.8  0.4 0.8  0.8 u00.4.8 = 0.52 = = 0.8 3  0.8  0.4 u 0.8 = 0.52 0.8 = 0.52 tietietotoChris = u 3 social Steve’s relationship Chrisisis So Steve’s So relationship 3 social social relationship tie to Chris is So Steve’s y S tie ( Steve, Chris ) S familiarit ( Steve, Chris ) u S reputation (Chris ) u S trust ( Steve, Chris ) Stietie (’sSteve , Chris ) S familiarit , Chris social relationship tiey (toSteve Chris is ) u S reputation (Chris ) u S trust ( Steve, Chris ) So Steve social) relationship to Chris is So S Steve ( Steve’s, Chris 0.8 u 0.52 utie 0.57 0.237 relationship tie to Chris is tie So Steve’s social familiarit y reputation S ( Steve , Chris ) S ( Steve (Chris ) u S trust ( Steve , Chris ) familiarit y , Chris ) u S S tie ( Steve , Chris ) S ( Steve , Chris ) u S reputation (Chris ) u S trust ( Steve, Chris ) tie familiarit y reputation Steve has) ustronger social tie, Chris to Chris S ( This Steveexample , Chris ) concludes S (that Steve , Chris S (Chrisrelationship ) u S trust ( Steve ) than to Albert. We can concludes that Steve hashis stronger socialwith relationship alsoThis inferexample that Chris is more likely to share knowledge Steve. tie to Chris than to Albert15.

>> > > >

familiarity

> > >

@ @ @

@@ @ @ @

15

We can also infer that Chris is more likely to share his knowledge with Steve.

5. Experiments and Discussions

5. Experiments and Discussions

15

15 15

Wehave haveconducted conductedquantitative quantitativeand andqualitative qualitativeexperiments experiments evaluate performance We to to evaluate thethe performance of of our social network-based PP search. We have developed a PP prototype, called SOtella as shown in Figure our social network-based P2P search. We have developed a P2P prototype, called SOtella as shown in Figure 3. SOtella is implemented based on an open-source software, Edutella (http://edutella.jxta.org/), which is an academic P2P framework equipped with a Resource Definition Framework (RDF)-based metadata to enhance resource description and discovery. In order to control the experiment scale and monitor SOtella’s performance, we confine the search

Journal of Scientific and Technological Studies, (), -(00)

12

. SOtella is implemented based on an open-source software, Edutella (http://edutella.jxta.org/), which is an academic PP framework equipped with a Resource Definition Framework (RDF)-based metadata to enhance resource description and discovery. In order to control the experiment scale and monitor SOtella’ s performance, we confine the search scope of SOtella to a small-scale network including about 0 peers within a university. Each peer in SOtella is associated with knowledge and social relationship ties as we presented in the three-layer social networks.

Figure 3. Screen shots of SOtella Fifty-six undergraduate students (junior) majored in computer science participated in this Figure . Screen shots of SOtella experiment, each installing SOtella on his/her own computer. In order to establish the three-layer Figure 3. Screen shots of SOtella social networks, each student students (peer) was asked majored to fill outinforms and answer questions to helpin this Fifty-six undergraduate (junior) computer Scientific participated experiment, each installing SOtella on his/her own computer. In order to establish three-layer SOtella identify and calculate peers’ knowledge and social relationship ties. the Adopting the social Fifty-six undergraduate students (junior) majored in computer science participated in thisand networks, each student in (peer) was asked to fill out forms andSOtella answer isquestions to help SOtella identify calculation presented this paper, a social network-based established. experiment, each installing on his/her ties. own Adopting computer.the In calculation order to establish the in three-layer calculate peers’ knowledge and SOtella social relationship presented this paper, a For quantitative performance evaluation, we measure two indexes: Precision and Recall. social network-based SOtella is established. social networks, each student (peer) was asked to fill out forms and answer questions to help Precision is the fraction of the found peers that considered as relevant; Recall the fraction For quantitative performance evaluation, we are measure two indexes: Precision andisRecall. Precision is SOtella identify and calculate peers’ knowledge and social relationship ties. Adopting the the fraction of the found that found. are considered relevant; is the fraction relevant peers of the relevant peers thatpeers has been Precisionasand RecallRecall are formally defined of as the follows: calculation presented in this paper, a social network-based SOtella is established. that has been found. Precision and Recall are formally defined as follows: For quantitative performance evaluation, we measure two indexes: Precision and Recall. Ra Ra Precision fraction Precision = is the , Recall = of the found peers that are considered as relevant; Recall is the fraction A R of the relevant peers that has been found. Precision and Recall are formally defined as follows: where, where, A contains a set of peers been found, |A| is the number of peers in A. A contains a set of peers been found, |A| is the number of peers in A. Ra been foundRa R contains a set peers that are considered relevant, |R| is the number of peers in R. Precision = , Recall = R Ra contains a set ofA peers as the intersection of the sets R and A. |Ra| is the number of peers in Ra. where, A contains a set of peers been found, |A| is the number of peers in A.

17

17

Ra contains a set of peers as the intersection of the sets R and A. |Ra| is the number of peers

in Ra. In this experiment, we used four kinds of domain knowledge as the search domains: Internet computing, Web computing, Mobile Internet, and Wireless Web. As indicated in Figure 4, for The Identification of Communities of Practice in Web .0 through Social Networks

the four given search domains, the Precision of social relationship tie (S-Tie) search outperforms

13

In knowledge this experiment, we used four kinds domain that knowledge aspeers the search domains: the relationship tie (K-Tie). Thisofindicates the found are more relevantInternet and computing, Web computing, Mobile Internet, and Wireless Web. As indicated in Figure , for the they are more likely to be in the same social group because they have higher social relationshipfour given search domains, the Precision of social relationship tie (S-Tie) search outperforms the knowledge tie. In contrast, the Recall of K-Tie search outperforms S-Tie. This indicates that peers with the relationship tie (K-Tie). This indicates that the found peers are more relevant and they are more likely to be same knowledge domain will most likelyhigher to besocial foundrelationship at the sametie. time have higher in the same social group because they have In because contrast,they the Recall of K-Tie knowledge relationship tie. indicates that peers with the same knowledge domain will most likely to be search outperforms S-Tie. This found at the same time because they have higher knowledge relationship tie. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

K-Tie (Precision) S-Tie (Precision) K-Tie (Recall) S-Tie (Recall)

Internet computing

Web computing

Mobile Internet

Wireless Web

Figure4..Precision Precisionand andRecall Recallas asperformed performedby by knowledge knowledge relationship Figure relationshiptie tie(K-Tie) (K-Tie)and andsocial social relationship tie (S-Tie) relationship tie (S-Tie) Besides the quantitative performance analysis, to understand the degree of users’ satisfactions to the Besides the quantitative performance analysis,whotoparticipated understandin this the experiment degree of tousers’ identified communities of practice, we asked every student complete a questionnaire their satisfaction level our social network-based PP.who Theparticipated survey reveals satisfactionstotomeasure the identified communities of with practice, we asked every student five findings. First, most of the participants whom found by SOtella match students’ needs in terms of in this experiment to complete a questionnaire to measure their satisfaction level with our social knowledge and social relationships. Second, students report that even the same search option may yield different results in different trials. We found the reason is that PP network only searches for participants currently on line. This symptom can be alleviated since the survey shows that most of the students 18 remain on line most of the time. Third, we found that most of the students are satisfied with the automatic identification of communities of practice. However, they still prefer to find their own participants, even though they admit that the communities of practice identified by SOtella are knowledgeable and close to their needs. This observation suggested that we should take into account students’ autonomy in addition to knowledge competence when we identifying communities of practice. Fourth, most of the students emphasize the importance of user interface design of social communication and collaboration. Fifth, we perceived that students desire powerful social networking software, such as Blogs, Wikis, RSS feeds, and video podcast for synchronous discussion and file sharing. In summary, our experiment confirmed the effectiveness of utilizing our social network-based PP search for identifying communities of practice in the Web .0. Most students expressed their willingness to utilize SOtella for their daily studies.

14

Journal of Scientific and Technological Studies, (), -(00)

6. Concluding Remarks The major contribution of this paper is applying social network to improve PP search by finding knowledgeable and socially related participants in Web .0. In this paper, we have presented a three-layer social network-based PP network equipped with the calculation of knowledge relationship tie and social relationship tie. By such a social network-based PP network, we demonstrated a new possibility of using social network to enhance PP so that query can be routed to peers with stronger relationship ties. We see several areas that deserve further research. Peers and other participants may have their own needs when they find participants and interact with others; therefore, we need to conduct further study of new relationship ties and investigate special requirements from different social perspectives in addition to knowledge and social relationships. It is also necessary to take into account collaboration context during the identification of communities of practice in Web .0.

Acknowledgement This work is supported by National Scientific Council, Taiwan under grants NSC-0-S-00-00MY.

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